fault tolerant hamiltonian laceability of balanced hypercubes the balanced hypercube as a new variant of hypercube has many desirable properties such as strong connectivity high regularity and symmetry the particular property of the balanced hypercube is that each processor has a backup processor sharing the same neighborhood a hamiltonian bipartite graph g v 0 v 1 e is said to be hamiltonian laceable if there is a hamiltonian path between any two vertices x v 0 and y v 1 it has been proved that the balanced hypercube bh n is hamiltonian laceable for all n 1 in this paper we have proved that after at most 2 n 2 faulty edges occur bh n remains hamiltonian laceable for all n 2 this result is optimal with respect to the number of faulty edges can be tolerated in bh n

matching preclusion for n dimensional torus networks abstract the matching preclusion number of a graph is the minimum number of edges whose deletion results in the remaining graph that has neither perfect matchings nor almost perfect matchings wang et al 13 proved that a class of n dimensional tours networks with even order are super matched later cheng et al 8 further showed that all n dimensional tours networks with even order are super matched in this paper we prove that all n dimensional torus networks with odd order are super matched if n 3 two dimensional torus networks with odd order is maximally matched except for c 3 c 3 our results are complementary to those of wang et al 13 and cheng et al 8

a fuzzy neural multi model for nonlinear systems identification and control the paper proposed to apply a hierarchical fuzzy neural multi model and takagi sugeno t s rules with recurrent neural procedural consequent part for systems identification states estimation and adaptive control of complex nonlinear plants the parameters and states of the local recurrent neural network models are used for a local direct and indirect adaptive trajectory tracking control systems design the designed local control laws are coordinated by a fuzzy rule based control system the upper level defuzzyfication is performed by a recurrent neural network the applicability of the proposed intelligent control system is confirmed by simulation examples and by a dc motor identification and control experimental results two main cases of a reference and plant output fuzzyfication are considered a two membership functions without overlapping and a three membership functions with overlapping in both cases a good convergent results are obtained

two disjoint cycle cover bipancyclicity of balanced hypercubes abstract a bipartite graph g is two disjoint cycle cover r1 r2 bipancyclic if for any even integer l satisfying r1 l r2 there exist two vertex disjoint cycles c1 and c2 in g such that v c 1 l and v c 2 v g l where v g denotes the number of vertices in g in this paper we study the two disjoint cycle cover bipancyclicity of the n dimensional balanced hypercube bhn which is a hypercube variant network and is superior to hypercube due to having a smaller diameter as a consequence we show that bhn is two disjoint cycle cover 4 2 2 n 1 bipancyclic for n 2

matching preclusion number in product graphs abstract the matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has no perfect matchings or almost perfect matchings for many interconnection networks the matching preclusion number is equal to the minimum degree of a vertex in the network and the sets of edges attaining the minimum are precisely those incident to a single vertex of minimum degree we say such networks are super matched in this paper we derive upper and lower bounds for the matching preclusion number for networks constructed using a variety of binary graph operations and give sufficient conditions for such networks to be super matched

strong matching preclusion for n dimensional torus networks the strong matching preclusion number of a graph is the minimum number of edges and or vertices whose deletion results in the remaining graph that has neither perfect matchings nor almost perfect matchings in 14 wang et al proved that c k c k is super strongly matched where k 3 is odd in this paper we show that c k 1 c k 2 c k n is super strongly matched where n 3 is an integer and k i 3 is an odd integer for each i 1 n our studies generalize the results of wang et al 14

a high order recurrent neuro fuzzy system with internal dynamics application to the adaptive noise cancellation a high order recurrent neuro fuzzy system ho rnfs is suggested in this paper suitable for modeling highly complex nonlinear temporal processes feedback connections are introduced in the network including the context and the feedback nodes that serve as a means to memorize the firing history the feedback paths in the firing loop are implemented through finite impulse response fir synaptic filters leading to a higher order network with enhanced temporal capabilities the inference mechanism of the ho rnfs is implemented by means of dynamic fuzzy rules where multiple steps ahead predictions are provided for the internal variables at the consequent part its structure is organized in an on line fashion using a concurrent structure and parameter algorithm structure learning generates dynamically the input and output clusters of the rules while parameter learning adjusts the network weights the ho rnfs is compared to the recurrent self organizing neural fuzzy inference network rsonfin being a special case of the suggested network the experimental setup includes a benchmark temporal system and the adaptive noise cancellation problem extensive experimentation reveals that ho rnfs exhibits superior speech enhancement performance as contrasted to rsonfin when complex noise passages are considered

fuzzy adaptive filters with application to nonlinear channel equalization two fuzzy adaptive filters are developed one uses a recursive least squares rls adaptation algorithm and the other uses a least mean square lms adaptation algorithm the rls fuzzy adaptive filter is constructed through the following four steps 1 define fuzzy sets in the filter input space rn whose membership functions cover u 2 construct a set of fuzzy if then rules which either come from human experts or are determined during the adaptation procedure by matching input output data pairs 3 construct a filter based on the set of rules and 4 update the free parameters of the filter using the rls algorithm the design procedure for the lms fuzzy adaptive filter is similar the most important advantage of the fuzzy adaptive filters is that linguistic information in the form of fuzzy if then rules and numerical information in the form of input output pairs can be combined in the filters in a uniform fashion the filters are applied to nonlinear communication channel equalization problems

a recurrent self evolving interval type 2 fuzzy neural network for dynamic system processing this paper proposes a recurrent self evolving interval type 2 fuzzy neural network rseit2fnn for dynamic system processing an rseit2fnn incorporates type 2 fuzzy sets in a recurrent neural fuzzy system in order to increase the noise resistance of a system the antecedent parts in each recurrent fuzzy rule in the rseit2fnn are interval type 2 fuzzy sets and the consequent part is of the takagi sugeno kang tsk type with interval weights the antecedent part of rseit2fnn forms a local internal feedback loop by feeding the rule firing strength of each rule back to itself the tsk type consequent part is a linear model of exogenous inputs the rseit2fnn initially contains no rules all rules are learned online via structure and parameter learning the structure learning uses online type 2 fuzzy clustering for the parameter learning the consequent part parameters are tuned by a rule ordered kalman filter algorithm to improve learning performance the antecedent type 2 fuzzy sets and internal feedback loop weights are learned by a gradient descent algorithm the rseit2fnn is applied to simulations of dynamic system identifications and chaotic signal prediction under both noise free and noisy conditions comparisons with type 1 recurrent fuzzy neural networks validate the performance of the rseit2fnn

recurrent neuro fuzzy networks for nonlinear process modeling a type of recurrent neuro fuzzy network is proposed in this paper to build long term prediction models for nonlinear processes the process operation is partitioned into several fuzzy operating regions within each region a local linear model is used to model the process the global model output is obtained through the centre of gravity defuzzification which is essentially the interpolation of local model outputs this modeling strategy utilizes both process knowledge and process input output data process knowledge is used to initially divide the process operation into several fuzzy operating regions and to set up the initial fuzzification layer weights process i o data are used to train the network network weights are such trained so that the long term prediction errors are minimized through training membership functions of fuzzy operating regions are refined and local models are learnt based on the recurrent neuro fuzzy network model a novel type of nonlinear model based long range predictive controller can be developed and it consists of several local linear model based predictive controllers local controllers are constructed based on the corresponding local linear models and their outputs are combined to form a global control action by using their membership functions this control strategy has the advantage that control actions can be calculated analytically avoiding the time consuming nonlinear programming procedures required in conventional nonlinear model based predictive control the techniques have been successfully applied to the modeling and control of a neutralization process

identification and prediction of dynamic systems using an interactively recurrent self evolving fuzzy neural network this paper presents a novel recurrent fuzzy neural network called an interactively recurrent self evolving fuzzy neural network irsfnn for prediction and identification of dynamic systems the recurrent structure in an irsfnn is formed as an external loops and internal feedback by feeding the rule firing strength of each rule to others rules and itself the consequent part in the irsfnn is composed of a takagi sugeno kang tsk or functional link based type the proposed irsfnn employs a functional link neural network flnn to the consequent part of fuzzy rules for promoting the mapping ability unlike a tsk type fuzzy neural network the flnn in the consequent part is a nonlinear function of input variables an irsfnns learning starts with an empty rule base and all of the rules are generated and learned online through a simultaneous structure and parameter learning an on line clustering algorithm is effective in generating fuzzy rules the consequent update parameters are derived by a variable dimensional kalman filter algorithm the premise and recurrent parameters are learned through a gradient descent algorithm we test the irsfnn for the prediction and identification of dynamic plants and compare it to other well known recurrent fnns the proposed model obtains enhanced performance results

modeling with discrete time recurrent fuzzy systems via mixed integer optimization in this paper we present a new approach for modeling of dynamical systems with discrete time recurrent fuzzy systems dtrfs the modeling problem is divided into two subproblems the first subproblem considers the identification of the rule base of the dtrfs which can be interpreted as structural modeling this problem is recast into integer and mixed integer optimization problems respectively dependent on the chosen cost function and system structure the second subproblem considers the optimization of the parameters of the system i e quantitative modeling as a result we obtain a linguistically interpretable model of the dynamical system which additionally can be interpreted as a linguistic automaton the applicability of the approach is shown by modeling a thermofluidic process

strong matching preclusion for k composition networks abstract let g 1 v 1 e 1 g 2 v 2 e 2 g k v k e k be k graphs and let f 1 v 1 v 2 f 2 v 2 v 3 f k 1 v k 1 v k f k v k v 1 be k bijections the k composition networks g induced by g 1 g 2 g k is the graph with v g t 1 k v g t and e g t 1 k e g t a t f t a t a t v g t and 1 t k many interconnection networks such as n dimensional torus networks recursive circulant graphs and cayley graphs on abelian groups generated by minimal generating sets are special k composition networks the strong matching preclusion number of a graph is the minimum number of edges and or vertices whose deletion results in the remaining graph has neither perfect matchings nor almost perfect matchings in this paper we study the strong matching preclusion number and strong matching preclusion sets for k composition networks with odd order our results generalize the main conclusion in 12

bipanconnectivity of balanced hypercubes the balanced hypercube proposed by wu and huang is a variant of the hypercube network in this paper paths of various lengths are embedded into balanced hypercubes a bipartite graph g is bipanconnected if for two arbitrary nodes x and y of g with distance d x y there exists a path of length l between x and y for every integer l with d x y l v g 1 and l d x y 0 mod 2 we prove that the n dimensional balanced hypercube bh n is bipanconnected for all n 1 this result is stronger than that obtained by xu et al which shows that the balanced hypercube is edge bipancyclic and hamiltonian laceable

edge pancyclicity and hamiltonian laceability of the balanced hypercubes abstract the balanced hypercube bh n is a variant of the hypercube q n huang and wu proved that bh n has better properties than q n with the same number of links and processors in particularly they showed that there exists a cycle of length 2 l in bh n for all l 2 l 2 n in this paper we improve this result by showing that bh n is edge pancyclic which means that for arbitrary edge e there exists a cycle of even length from 4 to 2 2 n containing e in bh n we also show that the balanced hypercubes are hamiltonian laceable

the balanced hypercube a cube based system for fault tolerant applications in this paper we present an interconnection structure called the balanced hypercube which is a variation of the standard hypercube with desirable properties of strong connectivity regularity and symmetry the proposed structure is a special type of load balanced graph designed to tolerate processor failure in balanced hypercubes each processor has a backup matching processor that shares the same set of neighboring nodes therefore tasks that run on a faulty processor can be reactivated in the backup processor to provide efficient system reconfiguration other properties of balanced hypercubes are examined it is also shown that odd dimensional balanced hypercubes have smaller diameters than that of standard hypercubes as an application of balanced hypercubes we show a fault tolerant embedding of rings in balanced hypercubes

diagonal recurrent neural networks for dynamic systems control a new neural paradigm called diagonal recurrent neural network drnn is presented the architecture of drnn is a modified model of the fully connected recurrent neural network with one hidden layer and the hidden layer comprises self recurrent neurons two drnn s are utilized in a control system one as an identifier called diagonal recurrent neuroidentifier drni and the other as a controller called diagonal recurrent neurocontroller drnc a controlled plant is identified by the drni which then provides the sensitivity information of the plant to the drnc a generalized dynamic backpropagation algorithm dbp is developed and used to train both drnc and drni due to the recurrence the drnn can capture the dynamic behavior of a system to guarantee convergence and for faster learning an approach that uses adaptive learning rates is developed by introducing a lyapunov function convergence theorems for the adaptive backpropagation algorithms are developed for both drni and drnc the proposed drnn paradigm is applied to numerical problems and the simulation results are included

strong matching preclusion for augmented cubes the strong matching preclusion number of a graph is the minimum number of vertices and edges whose deletion results in a graph that has neither perfect matchings nor almost perfect matchings the concept was introduced by park and son in this paper we study the strong matching preclusion problem for the augmented cube graphs as a result we find smp aqn and classify all optimal solutions

a mutually recurrent interval type 2 neural fuzzy system mrit2nfs with self evolving structure and parameters in this paper a mutually recurrent interval type 2 neural fuzzy system mrit2nfs is proposed for the identification of nonlinear and time varying systems the mrit2nfs uses type 2 fuzzy sets in order to enhance noise tolerance of the system in the mrit2nfs the antecedent part of each recurrent fuzzy rule is defined using interval type 2 fuzzy sets and the consequent part is of the takagi sugeno kang type with interval weights the antecedent part of mrit2nfs forms a local internal feedback and interaction loop by feeding the rule firing strength of each rule to others including itself the consequent is a linear combination of exogenous input variables the learning of mrit2nfs starts with an empty rule base and all rules are learned online via structure and parameter learning the structure learning of mrit2nfs uses online type 2 fuzzy clustering for parameter learning the consequent part parameters are tuned by rule ordered kalman filter algorithm to reinforce parameter learning ability the type 2 fuzzy sets in the antecedent and weights representing the mutual feedback are learned by the gradient descent algorithm after the training a weight elimination scheme eliminates feedback connections that do not have much effect on the network behavior this method can efficiently remove redundant recurrence and interaction weights finally the mrit2nfs is used for system identification under both noise free and noisy environments for this we consider both time series prediction and nonlinear plant modeling compared with type 1 recurrent fuzzy neural networks simulation results show that our approach produces smaller root mean squared errors using the same number of iterations

identification and control of dynamic systems using recurrent fuzzy neural networks proposes a recurrent fuzzy neural network rfnn structure for identifying and controlling nonlinear dynamic systems the rfnn is inherently a recurrent multilayered connectionist network for realizing fuzzy inference using dynamic fuzzy rules temporal relations are embedded in the network by adding feedback connections in the second layer of the fuzzy neural network fnn the rfnn expands the basic ability of the fnn to cope with temporal problems in addition results for the fnn fuzzy inference engine universal approximation and convergence analysis are extended to the rfnn for the control problem we present the direct and indirect adaptive control approaches using the rfnn based on the lyapunov stability approach rigorous proofs are presented to guarantee the convergence of the rfnn by choosing appropriate learning rates finally the rfnn is applied in several simulations time series prediction identification and control of nonlinear systems the results confirm the effectiveness of the rfnn

a recurrent self evolving fuzzy neural network with local feedbacks and its application to dynamic system processing this paper proposes a recurrent self evolving fuzzy neural network with local feedbacks rsefnn lf for dynamic system processing a rsefnn lf is composed of zero order or first order takagi sugeno kang tsk type recurrent fuzzy if then rules the recurrent structure in a rsefnn lf comes from locally feeding the firing strength of a fuzzy rule back to itself a rsefnn lf is constructed on line via simultaneous structure and parameter learning in structure learning an efficient rule and fuzzy set generation algorithm is proposed to generate fuzzy rules on line and reduce the number of fuzzy sets in each dimension in parameter learning the consequent part parameters are learned through a varying dimensional kalman filter algorithm whose input dimension varies with structure learning the antecedent part and feedback loop parameters are learned using a gradient descent algorithm the rsefnn lf is applied to dynamic system identification chaotic sequence prediction and speech recognition problems this paper also compares the performance of the rsefnn lf with other recurrent fuzzy neural networks

hamiltonian cycle embedding for fault tolerance in balanced hypercubes abstract the balanced hypercube bh n defined by wu and huang is a variant of the hypercube network yang proposed that fault tolerance of balanced hypercube bh n is an important issue in parallel computing which needs further study yang 2010 24 in this paper we prove that there exists a fault free hamiltonian path between any two adjacent vertices in bh n with 2 n 2 faulty edges as a corollary we derive that for any fault free edge e there exists a fault free hamiltonian cycle containing e in bh n with 2 n 2 faulty edges which is optimal in the sense of the number of faulty edges

adaptive noise cancellation for a class of non linear iir filters abstract a new filter structure known as the narmax model non linear auto regressive moving average model with exogenous inputs is introduced as a basis for the design of a class of non linear filters which can be used for noise cancellation the new design is an extension of the classical iir filter and it is shown that the filter parameters can be estimated by using a suboptimal least squares algorithm originally developed for system identification conditions for the convergence of the algorithm are briefly discussed and simulation results are presented which show the effectiveness of the new structure

conditional matching preclusion for the arrangement graphs the matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has neither perfect matchings nor almost perfect matchings for many interconnection networks the optimal sets are precisely those induced by a single vertex recently the conditional matching preclusion number of a graph was introduced to look for obstruction sets beyond those induced by a single vertex it is defined to be the minimum number of edges whose deletion results in a graph with no isolated vertices that has neither perfect matchings nor almost perfect matchings in this paper we find this number and classify all optimal sets for the arrangement graphs one of the most popular interconnection networks

fault tolerant strong menger edge connectivity and 3 extra edge connectivity of balanced hypercubes abstract a connected graph g is called strongly menger edge connected if for any two distinct vertices x y of g there are min deg g x deg g y edge disjoint paths between x and y the g g 0 extra edge connectivity of the connected graph g denoted by g g is the minimal cardinality of a set of edges of g if exists whose deletion disconnects g and each remaining component contains more than g vertices in this paper we show that the n dimensional balanced hypercube b h n which is a variant of hypercube q n is still strongly menger edge connected even when there are 2 n 4 faulty vertices resp 2 n 2 faulty edges for n 2 moreover under the restricted condition that each vertex has at least two fault free edges we show that b h n is still strongly menger edge connected even when there are 6 n 8 faulty edges for n 2 these results are all optimal with respect to the maximum number of tolerated edge faults furthermore we showed that the 3 extra edge connectivity of b h n is 8 n 8 for n 2

takagi sugeno fuzzy modeling incorporating input variables selection fuzzy models especially takagi sugeno t s fuzzy models have received particular attention in the area of nonlinear modeling due to their capability to approximate any nonlinear behavior based only on measured data without any prior knowledge there is no systematic way to obtain a t s fuzzy model with a simple structure and sufficient accuracy the main idea discussed in this paper is to reduce the complexity of t s fuzzy models by estimating an optimal number of fuzzy rules and selecting relevant inputs as antecedent variables independently of the selection of consequent regressors a systematic procedure is proposed here and illustrated on static and dynamical nonlinear systems

vibration of open circular cylindrical shells with intermediate ring supports abstract this paper is concerned with the free vibration of open circular cylindrical shells with intermediate ring supports an analytical procedure for determining the free vibration frequencies of such shells is developed based on the flugge thin shell theory an open circular cylindrical shell is assumed to be simply supported along the two straight edges and the remaining two opposite curved edges may have any combinations of support conditions the shell is divided into multiple segments along the locations of the intermediate ring supports the state space technique is employed to derive the exact solutions for each shell segment and the domain decomposition method is applied to enforce the geometric and natural boundary interface conditions along the interfaces of the shell segments and the curved edges of the shell comparison studies are carried out to verify the correctness of the proposed method exact vibration frequencies are obtained for open circular cylindrical shells with multiple intermediate ring supports the influence of the number of intermediate ring supports the locations of the ring supports the boundary conditions and the variation of the included angle of the shells on the natural frequencies are examined the exact vibration solutions can be used as important benchmark values for researchers to check their numerical methods and for engineers to design such shell structures

on the free vibration of completely free open cylindrically curved isotropic shell panels abstract theh pversion of the finite element method is used to furnish a detailed study of the vibration characteristics of completely free open cylindrically curved isotropic shell panels convergence studies have been carried out to ascertain the most appropriate mix ofh refinement andp enrichment necessary to give a reasonable solution accuracy this approach has been corroborated by comparing results from the present methodology with the work of other investigators and the results forthcoming from a proprietary finite element package extensive results have been presented for panels with aspect ratios of 0 5 1 and 2 the curvature of which is gradually increased from that of a flat plate at one extreme to a complete although unjoined cylinder at the other curves of frequency versus included angle curvature have been obtained for the first 20 modes of each of these panels and the following observations have been made i the majority of the low frequency modes are virtually independent of shell curvature and ii the modes that are heavily influenced by curvature effects involve motion that is confined to the lengthwise edge regions of the panel

non linear free periodic vibrations of open cylindrical shallow shells this paper is concerned with the non linear free periodic vibrations of thin open cylindrical and shallow shells vibrating in the geometrically non linear regime a multi degree of freedom model with hierarchical basis functions is adopted and the principle of the virtual work is used to define the time domain equations of motion these equations are transformed into the frequency domain by the harmonic balance method and are finally solved by an arc length continuation method shells of different thicknesses and of different curvature radius are analysed and the variation of the non linear natural frequencies of these shells with the vibration amplitude are investigated in some detail the variation of the mode shapes with the vibration amplitude is demonstrated it is found that both softening and hardening spring effects occur and that the number of couplings between vibration modes is rather large in undamped shells

strongly menger edge connectedness and strongly menger vertex connectedness of regular networks abstract let f be a conditional faulty edge set of a graph g here the condition imposed on f is g f r for some fixed integer r the graph g is called f strongly menger edge connected of order r if each pair of distinct vertices u and v are connected by min deg g f u deg g f v edge disjoint paths in g f where deg g f u and deg g f v are the degrees of u and v in g f respectively a graph g is t strongly menger edge connected of order r if g is f strongly menger edge connected of order r for every f e g with f t one can consider the vertex version in an analogous way there are a number of papers with results for r 2 for several popular classes of interconnection network in this paper we develop a unified approach by considering various sufficient conditions of a regular graph to be f strongly menger edge connected of order r and the corresponding vertex version as corollaries several results on fundamental classes of interconnection networks are presented including new results as well as strengthening of existing results such as the one given by qiao and yang 2017 20

structure identification of generalized adaptive neuro fuzzy inference systems this paper presents a method to identify the structure of generalized adaptive neuro fuzzy inference systems ganfiss the structure of ganfis consists of a number of generalized radial basis function grbf units the radial basis functions are irregularly distributed in the form of hyper patches in the input output space the minimum number of grbf units is selected based on a heuristic using the fuzzy curve for structure identification a new criterion called structure identification criterion sic is proposed sic deals with a trade off between performance and computational complexity of the ganfis model the computational complexity of gradient descent learning is formulated based on simulation study three methods of initialization of ganfis viz fuzzy curve fuzzy c means in x spl times y space and modified mountain clustering have been compared in terms of cluster validity measure akaike s information criterion aic and the proposed sic

a locally recurrent fuzzy neural network with support vector regression for dynamic system modeling this paper proposes a new recurrent model known as the locally recurrent fuzzy neural network with support vector regression lrfnn svr that handles problems with temporal properties structurally an lrfnn svr is a five layered recurrent network the recurrent structure in an lrfnn svr comes from locally feeding the firing strength of each fuzzy rule back to itself the consequent layer in an lrfnn svr is a takagi sugeno kang t s k type consequent which is a linear function of current states regardless of system input and output delays for the structure learning a one pass clustering algorithm clusters the input training data and determines the number of network nodes in hidden layers for the parameter learning an iterative linear svr algorithm is proposed to tune free parameters in the rule consequent part and feedback loops the motivation for using svr for parameter learning is to improve the lrfnn svr generalization ability this paper demonstrates lrfnn svr capabilities by conducting simulations in dynamic system prediction and identification problems with noiseless and noisy data in addition this paper compares simulation results from the lrfnn svr with other recurrent fuzzy models

a fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks a fast approach for automatically generating fuzzy rules from sample patterns using generalized dynamic fuzzy neural networks gd fnns is presented the gd fnn is built based on ellipsoidal basis functions and functionally is equivalent to a takagi sugeno kang fuzzy system the salient characteristics of the gd fnn are 1 structure identification and parameters estimation are performed automatically and simultaneously without partitioning input space and selecting initial parameters a priori 2 fuzzy rules can be recruited or deleted dynamically 3 fuzzy rules can be generated quickly without resorting to the backpropagation bp iteration learning a common approach adopted by many existing methods the gd fnn is employed in a wide range of applications ranging from static function approximation and nonlinear system identification to time varying drug delivery system and multilink robot control simulation results demonstrate that a compact and high performance fuzzy rule base can be constructed comprehensive comparisons with other latest approaches show that the proposed approach is superior in terms of learning efficiency and performance

conditional matching preclusion for hypercube like interconnection networks the conditional matching preclusion number of a graph with n vertices is the minimum number of edges whose deletion results in a graph without an isolated vertex that does not have a perfect matching if n is even or an almost perfect matching if n is odd we develop some general properties on conditional matching preclusion and then analyze the conditional matching preclusion numbers for some hl graphs hypercube like interconnection networks

edge fault tolerant edge bipancyclicity of balanced hypercubes abstract the balanced hypercube bh n is a variant of hypercube q n hao et al 2014 showed that there exists a fault free hamiltonian path between any two adjacent vertices in bh n with 2 n 2 faulty edges cheng et al 2015 proved that bh n is 6 edge bipancyclic after 2 n 3 faulty edges occur for all n 2 in this paper we improve these two results by demonstrating that bh n is 6 edge bipancyclic even when there exist 2 n 2 faulty edges for all n 2 our result is optimal with respect to the maximum number of tolerated edge faults

a recurrent self organizing neural fuzzy inference network a recurrent self organizing neural fuzzy inference network rsonfin is proposed the rsonfin is inherently a recurrent multilayered connectionist network for realizing the basic elements and functions of dynamic fuzzy inference and may be considered to be constructed from a series of dynamic fuzzy rules the temporal relations embedded in the network are built by adding some feedback connections representing the memory elements to a feedforward neural fuzzy network each weight as well as node in the rsonfin has its own meaning and represents a special element in a fuzzy rule there are no hidden nodes initially in the rsonfin they are created online via concurrent structure identification and parameter identification the structure learning together with the parameter learning forms a fast learning algorithm for building a small yet powerful dynamic neural fuzzy network two major characteristics of the rsonfin can thus be seen 1 the recurrent property of the rsonfin makes it suitable for dealing with temporal problems and 2 no predetermination like the number of hidden nodes must be given since the rsonfin can find its optimal structure and parameters automatically and quickly moreover to reduce the number of fuzzy rules generated a flexible input partition method the aligned clustering based algorithm is proposed various simulations on temporal problems are done and performance comparisons with some existing recurrent networks are also made efficiency of the rsonfin is verified from these results

hamiltonian paths passing through prescribed edges in balanced hypercubes abstract the balanced hypercube was proposed as a novel interconnection network for large scale parallel systems it is known that the balanced hypercube is edge bipancyclic given a set of edges p with p n 1 and two vertices x and y in different bipartite sets we show that there exists a hamiltonian path from x to y passing through p in b h n if and only if p is a linear forest and neither x nor y is an internal vertex of p and x and y are not end vertices of a component of p simultaneously consequently the balanced hypercube contains a hamiltonian cycle passing through a set p of at most n prescribed edges if and only if p is a linear forest moreover our result improves some known results

matching preclusion for balanced hypercubes matching preclusion is a measure of robustness in the event of edge failure in interconnection networks the matching preclusion number of a graph g is the minimum number of edges whose deletion leaves the resulting graph without a perfect matching or an almost perfect matching and the conditional matching preclusion number of g is the minimum number of edges whose deletion leaves the resulting graph with no isolated vertices and without a perfect matching or an almost perfect matching in this paper we consider balanced hypercubes we obtain that an n dimension balanced hypercube bh n has the matching preclusion number 2n and mainly prove that for the balanced hypercube bh n each matching preclusion set of cardinality 2n is trivial and the conditional matching preclusion number of balanced hypercube is 4n 2 whenever n 2

an online self constructing neural fuzzy inference network and its applications a self constructing neural fuzzy inference network sonfin with online learning ability is proposed in this paper the sonfin is inherently a modified takagi sugeno kang tsk type fuzzy rule based model possessing neural network learning ability there are no rules initially in the sonfin they are created and adapted as online learning proceeds via simultaneous structure and parameter identification in the structure identification of the precondition part the input space is partitioned in a flexible way according to an aligned clustering based algorithm as to the structure identification of the consequent part only a singleton value selected by a clustering method is assigned to each rule initially afterwards some additional significant terms selected via a projection based correlation measure for each rule will be added to the consequent part incrementally as learning proceeds the combined precondition and consequent structure identification scheme can set up an economic and dynamically growing network a main feature of the sonfin in the parameter identification the consequent parameters are tuned optimally by either least mean squares or recursive least squares algorithms and the precondition parameters are tuned by a backpropagation algorithm to enhance the knowledge representation ability of the sonfin a linear transformation for each input variable can be incorporated into the network so that much fewer rules are needed or higher accuracy can be achieved

new formulation for representing higher order tsk fuzzy systems this paper introduces a recursive relation between an existing takagi sugeno kang tsk fuzzy system of order n and systems of order n 1 and 0 the presented method helps to realize higher order tsk fuzzy systems using first and zero order tsk fuzzy system models which are available in simulation software e g matlab and its toolboxes this method has two major benefits 1 easier and faster simulation of higher order tsk systems using available software and 2 possibility of tuning high order tsk systems by methods like anfis which can only tune first and zero order tsk systems the final section of this paper includes a comparison of simulation speed if the proposed method is used or not and nonlinear function estimation by applying anfis on high order tsk system using the proposed method simulation results prove the superiority and usefulness of the proposed method

various cycles embedding in faulty balanced hypercubes consider the balanced hypercube bh n with f e 2 n 3 faulty edges prove that every edge of bh n f e lies on fault free cycles of even lengths from 6 to 22n prove that the lower limit of the length 6 is sharp quite a lot of interconnection networks are served as the underlying topologies of large scale multiprocessor systems the hypercube is one of the most popular interconnection networks in this paper we consider the balanced hypercube which is a variant of the hypercube huang and wu showed that the balanced hypercube has better properties than hypercube with the same number of links and processors let f e be the set of faulty edges in an n dimensional balanced hypercube bh n where n 2 in this paper we consider bh n with f e 2 n 3 faulty edges and prove that every fault free edge lies on a fault free cycle of every even length from 6 to 2 2 n in bh n f e furthermore we prove that the lower limit of the length 6 is sharp by giving a counter example

generalization of adaptive neuro fuzzy inference systems the adaptive network based fuzzy inference systems anfis of jang 1993 is extended to the generalized anfis ganfis by proposing a generalized fuzzy model gfm and considering a generalized radial basis function grbf network the gfm encompasses both the takagi sugeno ts model and the compositional rule of inference cri model the conditions by which the proposed gfm converts to ts model or the cri model are presented the basis function in grbf is a generalized gaussian function of three parameters the architecture of the grbf network is devised to learn the parameters of gfm where the grbf network and gfm have been proved to be functionally equivalent it is shown that grbf network can be reduced to either the standard rbf or the hunt s rbf network the issue of the normalized versus the non normalized grbf networks is investigated in the context of ganfis an interesting property of symmetry on the error surface of grbf network is investigated the proposed ganfis is applied to the modeling of a multivariable system like stock market

a note on generalized matching preclusion in bipartite graphs abstract for a graph with an even number of vertices the matching preclusion number is the minimum number of edges whose deletion results in a graph with no perfect matchings the conditional matching preclusion number introduced as an extension of the matching preclusion number has the additional requirement that the resulting graph have no isolated vertices in bipartite graphs the edge sets that achieve these minimum numbers can be construed in terms of obstruction sets of vertices from this perspective the generalized matching preclusion problem was developed the matching preclusion number of order a is the minimum number of edges whose deletion results in a graph with no perfect matchings and no obstruction sets with fewer than a vertices in this paper we extend known sufficient conditions regarding the matching preclusion and conditional matching preclusion numbers orders 1 and 2 respectively to give sufficient conditions for the matching preclusion problem of order a for any positive integer a

theory and experiments for large amplitude vibrations of circular cylindrical panels with geometric imperfections abstract large amplitude vibrations of circular cylindrical panels open shells subjected to harmonic excitation are numerically and experimentally investigated the donnell nonlinear strain displacement relationships are used to describe the geometric nonlinearity in plane inertia is taken into account specific boundary conditions with zero transverse displacement at the panel edges and free or elastically restrained in plane displacements not previously considered have been introduced in order to model the experimental boundary conditions the nonlinear equations of motion are obtained by the lagrange equations with multi mode approach and are studied by using a code based on the pseudo arclength continuation method two thin circular cylindrical panels of different dimensions and made of stainless steel have been experimentally tested in the laboratory for several excitation amplitudes in order to characterize the nonlinearity the dimensions of the two panels have been chosen in order to have the fundamental mode with one and two circumferential half waves respectively numerical results are able to reproduce the experimental results with high accuracy for both panels the effect of geometric imperfections on the trend of nonlinearity and on natural frequencies is shown convergence of the solution with the number of generalized coordinates is numerically verified

qualitative modeling of dynamical systems employing continuous time recurrent fuzzy systems continuous time recurrent fuzzy systems ctrfs allow the representation of knowledge based dynamic processes that can be stated in the form of if then rules in this article we show how a ctrfs can not only be modeled by linguistically given knowledge but also by measured data furthermore a unified approach for both structure and parameter identification of continuous time recurrent fuzzy systems will be presented resulting in a linguistically interpretable model of the considered dynamic process the capability of the approach is shown by modeling of a biotechnological process

multi level adaptive neuro fuzzy inference system based reconstruction of 1d isomap representations abstract this paper addresses the inverse problem of isometric feature mapping isomap via multi level adaptive neuro fuzzy inference system ml anfis isomap is a conventional nonlinear dimensionality reduction nldr method which prospects for low dimensional interior structure embedded in high dimensional data space the inverse problem of isomap reconstructs the original high dimensional data from the related low dimensional isomap representations and holds promising applications in data representations generation compression and visualization because the reconstruction of 1d isomap representations is ill posed and undetermined ml anfis is wielded to augment the recovery quality of general isomap reconstruction algorithm by linearly combing inputs with nonlinear weights as output ml anfis can efficiently achieve the latent nonlinear relationship between the low performance result of general isomap reconstruction algorithm and its original data the membership functions and fuzzy rules of ml anfis can be automatically learned by gradient descent method as deep learning it is demonstrated by the experimental results that in the situation of 1d representations the proposed method is superior to the state of the art methods such as nearest neighbor nn discrete cosine transformation dct sparse representations sr and classical anfis algorithms in the reconstruction performance of video data

matching preclusion and conditional matching preclusion problems for the folded petersen cube the matching preclusion number of an even graph is the minimum number of edges whose deletion results in a graph that has no perfect matchings for many interconnection networks the optimal sets are precisely those induced by a single vertex recently the conditional matching preclusion number of an even graph was introduced to look for obstruction sets beyond those induced by a single vertex it is defined to be the minimum number of edges whose deletion results in a graph with no isolated vertices and no perfect matchings in this paper we study this problem for the folded petersen cube fpq n k via some matching preclusion and conditional matching preclusion results of the cartesian products of graphs

conditional matching preclusion sets abstract the matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has neither perfect matchings nor almost perfect matchings for many interconnection networks the optimal sets are precisely those induced by a single vertex in this paper we look for obstruction sets beyond these sets we introduce the conditional matching preclusion number of a graph it is the minimum number of edges whose deletion results in a graph with no isolated vertices that has neither perfect matchings nor almost perfect matchings we find this number and classify all optimal sets for several basic classes of graphs

strong matching preclusion the matching preclusion problem introduced by brigham et al r c brigham f harary e c violin and j yellen perfect matching preclusion congressus numerantium 174 2005 185 192 studies how to effectively make a graph have neither perfect matchings nor almost perfect matchings by deleting as small a number of edges as possible extending this concept we consider a more general matching preclusion problem called the strong matching preclusion in which deletion of vertices is additionally permitted we establish the strong matching preclusion number and all possible minimum strong matching preclusion sets for various classes of graphs