is a single or double arm technique more advantageous in triple jumping triple jumpers employ either an asymmetrical single arm action or symmetrical double arm action in the takeoff of each phase of the jump this study investigated which technique is more beneficial in each phase using computer simulation kinematic data were obtained from an entire triple jump using a vicon automatic motion capture system a planar 13 segment torque driven subject specific computer simulation model was evaluated by varying torque generator activation timings using a genetic algorithm in order to match performance data the matching produced a close agreement between simulation and performance with differences of 3 8 2 7 and 3 1 for the hop step and jump phases respectively each phase was optimised for jump distance and an increase in jump distance beyond the matched simulations of 3 3 11 1 and 8 2 was obtained for the hop step and jump respectively the optimised technique used symmetrical shoulder flexion whereas the triple jumper had used an asymmetrical arm technique this arm action put the leg extensors into slower concentric conditions allowing greater extensor torques to be produced the main increases in work came at the joints of the stance leg but the largest increases in angular impulse came at the shoulder joints indicating the importance of both measures when assessing the impact of individual joint actions on changes in technique possible benefits of the double arm technique include cushioning the stance leg during impact raising the centre of mass of the body at takeoff facilitating an increase in kinetic energy at takeoff allowing a re orientation of the body during flight

fuzzy rule selection by multi objective genetic local search algorithms and rule evaluation measures in data mining this paper shows how a small number of simple fuzzy if then rules can be selected for pattern classification problems with many continuous attributes our approach consists of two phases candidate rule generation by rule evaluation measures in data mining and rule selection by multi objective evolutionary algorithms in our approach first candidate fuzzy if then rules are generated from numerical data and prescreened using two rule evaluation measures i e confidence and support in data mining then a small number of fuzzy if then rules are selected from the prescreened candidate rules using multi objective evolutionary algorithms in rule selection we use three objectives maximization of the classification accuracy minimization of the number of selected rules and minimization of the total rule length thus the task of multi objective evolutionary algorithms is to find a number of non dominated rule sets with respect to these three objectives the main contribution of this paper is to propose an idea of utilizing the two rule evaluation measures as prescreening criteria of candidate rules for fuzzy rule selection an arbitrarily specified number of candidate rules can be generated from numerical data for high dimensional pattern classification problems through computer simulations we demonstrate that such a prescreening procedure improves the efficiency of our approach to fuzzy rule selection we also extend a multi objective genetic algorithm moga in our former studies to a multi objective genetic local search mogls algorithm where a local search procedure adjusts the selection i e inclusion or exclusion of each candidate rule furthermore a learning algorithm of rule weights i e certainty factors is combined with our mogls algorithm such extensions to our moga for fuzzy rule selection are another contribution of this paper

sequential pattern recognition employing recurrent fuzzy systems sequential pattern recognition systems check whether data strings e g time series exhibit certain pattern primitives in a specified order as in the case of most other pattern recognition methods either conventional methods or fuzzy systems may be used here this paper presents a sequential pattern recognition system employing recurrent fuzzy systems that is employed as a monitoring system on continuous casting systems in the steel industry worldwide taking that application as a starting point a general method for sequential pattern recognition in time series that uses recurrent fuzzy systems is described

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

the effect of increasing strength and approach velocity on triple jump performance the triple jump is an athletic event comprising three phases in which the optimal phase ratio the proportion of each phase to the total distance jumped is unknown this study used a planar whole body torque driven computer simulation model of the ground contact parts of all three phases of the triple jump to investigate the effect of strength and approach velocity on optimal performance the strength and approach velocity of the simulation model were each increased by up to 30 in 10 increments from baseline data collected from a national standard triple jumper increasing strength always resulted in an increased overall jump distance increasing approach velocity also typically resulted in an increased overall jump distance but there was a point past which increasing approach velocity without increasing strength did not lead to an increase in overall jump distance increasing both strength and approach velocity by 10 20 and 30 led to roughly equivalent increases in overall jump distances distances ranged from 14 05m with baseline strength and approach velocity up to 18 49m with 30 increases in both optimal phase ratios were either hop dominated or balanced and typically became more balanced when the strength of the model was increased by a greater percentage than its approach velocity the range of triple jump distances that resulted from the optimisation process suggests that strength and approach velocity are of great importance for triple jump performance

effect of arm swing direction on forward and backward jump performance this study quantified and compared how the directional differences in arm swing affected mechanical and physiological parameters during forward and backward jumping seven subjects maximally performed three types of forward and backward squat jumps no arm swing fj bj forward arm swing fjf bjf and backward arm swing fjb bjb from a force platform all performances were captured with a 3 d motion capture system electromyograms emgs of the lower extremity muscles were obtained variables were calculated by combining kinematic and kinetic data the jump displacement and center of mass velocity at take off were significantly larger in fjf than in fj or fjb and larger in bjb than in bj or bjf suggesting that the best performance was obtained by employing the same arm swing direction as a given jump direction the total work by three lower and two upper extremity joints was significantly larger in fjf than in fj or fjb and larger in bjb than in bj or bjf for the lower extremity joints hip work was the greatest in fjf and bjb the integrated emg of the biceps femoris when the hip power was produced was significantly larger in fjf and bjb than under other conditions these results suggest that if the arm swing direction is the same as a given jump direction the activation level of the hip extensor is greater to counter large loads which make the hip joint flex during the push off phase which result in increased hip extension torque power and work

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

ten years of genetic fuzzy systems current framework and new trends fuzzy systems have demonstrated their ability to solve different kinds of problems in various application domains currently there is an increasing interest to augment fuzzy systems with learning and adaptation capabilities two of the most successful approaches to hybridise fuzzy systems with learning and adaptation methods have been made in the realm of soft computing neural fuzzy systems and genetic fuzzy systems hybridise the approximate reasoning method of fuzzy systems with the learning capabilities of neural networks and evolutionary algorithms the objective of this paper is to provide an account of genetic fuzzy systems with special attention to genetic fuzzy rule based systems after a brief introduction to models and applications of genetic fuzzy systems the field is overviewed new trends are identified a critical evaluation of genetic fuzzy systems for fuzzy knowledge extraction is elaborated and open questions that remain to be addressed in the future are raised the paper also includes some of the key references required to quickly access implementation details of genetic fuzzy systems

regularity and chaos in recurrent fuzzy systems abstract in this paper we shall present a mathematical definition of recurrent fuzzy systems and begin to systematically investigate the underlying theory involved unlike static fuzzy systems recurrent fuzzy systems are equipped with time delayed feedback of their output and allow representing knowledge based dynamic processes that may be stated in the form of if then rules we study their relationship to automata and show that they have an automaton like behavior when appropriately designed in other cases recurrent fuzzy system may exhibit chaotic behavior we present sufficient conditions for the occurrence of chaos in recurrent fuzzy systems that can easily be checked solely on the basis of the qualitative linguistically formulated models we also discuss the extent to which state graphs may be used for describing the behaviors of recurrent fuzzy systems

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

determination of subject specific model parameters for visco elastic elements the determination of subject specific model parameter values is necessary in order for a computer simulation model of human motion to be evaluated quantitatively this study used an optimisation procedure along with a kinematically driven simulation model of the contact phase in running jumps to determine the elastic parameters of segmental wobbling masses and the foot ground interface kinetic and kinematic data were obtained on running jumps for height and distance performed by an elite male high jumper stiffness and damping coefficients of the visco elastic elements in the model were varied until the difference between simulation and performance was minimised percentage differences of 6 and 9 between the simulated and recorded performances were obtained in the jumps for height and distance respectively when the parameters obtained from the jump for height were used in a simulation of the jump for distance and vice versa there was poor agreement with the recorded jump on the other hand a common set of visco elastic parameters were obtained using the data from both recorded jumps resulting in a mean difference of only 8 made up of 7 and 10 between simulation and performance that was almost as good as the individual matches simulations were not overly sensitive to perturbations of the common set of visco elastic parameters it is concluded that subject specific elastic parameters should be calculated from more than a single jump in order to provide a robust set of values that can be used in different simulations

wavelet approximation based affine invariant shape representation functions in this paper new wavelet based affine invariant functions for shape representation are presented unlike the previous representation functions only the approximation coefficients are used to obtain the proposed functions one of the derived functions is computed by applying a single wavelet transform the other function is calculated by applying two different wavelet transforms with two different wavelet families one drawback of the previously derived detail based invariant representation functions is that they are sensitive to noise at the finer scale levels which limits the number of scale levels that can be used the experimental results in this paper demonstrate that the proposed functions are more stable and less sensitive to noise than the detail based functions

role of arm motion in the standing long jump the role of arm motion on the performance of the standing long jump was investigated three males performed a series of jumps with free jfa and with restricted jra arm motion to determine if arm swing improves jumping distance the subjects jumped off a force platform and the motion of the body segments were recorded with a four camera passive motion capture system jumping performance was defined as the horizontal displacement of the toe between the initial and landing td positions the subjects jumped 21 2 further on an average with arm movement 2 09 0 03 m than without 1 72 0 03 m seventy one percent of the increase in performance in jfa was attributable to a 12 7 increase in the take off to velocity of the center of gravity cg increases in the horizontal displacement of the cg before to and in the horizontal position of the toe with respect to the cg at td accounted for the remaining 29 of the improvement in jumping distance the added balance and control provided by the arms throughout the jumping motion contributed to performance improvement in jfa the subjects were able to remedy excessive forward rotation about the cg by swinging the arms backwards during the flight phase without the freedom to swing the arms during flight the subjects had to eliminate any excessive forward rotation while still in contact with the ground this tendency in jra was manifest in the premature decline in the vertical ground reaction force vgrf and the development of a counterproductive backward rotating moment about the cg just before to

control of systems integrating logic dynamics and constraints this paper proposes a framework for modeling and controlling systems described by interdependent physical laws logic rules and operating constraints denoted as mixed logical dynamical mld systems these are described by linear dynamic equations subject to linear inequalities involving real and integer variables mld systems include linear hybrid systems finite state machines some classes of discrete event systems constrained linear systems and nonlinear systems which can be approximated by piecewise linear functions a predictive control scheme is proposed which is able to stabilize mld systems on desired reference trajectories while fulfilling operating constraints and possibly take into account previous qualitative knowledge in the form of heuristic rules due to the presence of integer variables the resulting on line optimization procedures are solved through mixed integer quadratic programming miqp for which efficient solvers have been recently developed some examples and a simulation case study on a complex gas supply system are reported

parameter identification of recurrent fuzzy systems with fuzzy finite state automata representation this paper presents the identification of nonlinear dynamical systems by recurrent fuzzy system rfs models two types of rfs models are discussed the takagi sugeno kang tsk type and the linguistic or mamdani type both models are equivalent and the latter model may be represented by a fuzzy finite state automaton ffa an identification procedure is proposed based on a standard general purpose genetic algorithm ga first the tsk rule parameters are estimated and in a second step the tsk model is converted into an equivalent linguistic model the parameter identification is evaluated in some benchmark problems for nonlinear system identification described in literature the results show that rfs models achieve good numerical performance while keeping the interpretability of the actual system dynamics

propositional logic in control and monitoring problems by using linear constraints on integer variables logical inference can be incorporated within model predictive control and model based detection problems resulting in mixed integer quadratic or mixed integer linear programming problems qualitative knowledge can be used to prioritize control objectives or to improve performance of detection schemes this article demonstrates some capabilities of combining logic using integer variables with quantitative models

understanding how an arm swing enhances performance in the vertical jump this investigation was conducted to examine the various theories that have been proposed to explain the enhancement of jumping performance when using an arm swing compared to when no arm swing is used twenty adult males were asked to perform a series of maximal vertical jumps while using an arm swing and again while holding their arms by their sides force motion and electromyographical data were recorded during each performance participants jumped higher 0 086 m in the arm swing compared to the no arm swing condition and was due to increased height 28 and velocity 72 of the center of mass at take off the increased height at take off was due to the elevation of the arm segments the increased velocity of take off stemmed from a complex series of events which allowed the arms to build up energy early in the jump and transfer it to the rest of the body during the later stages of the jump this energy came from the shoulder and elbow joints as well as from extra work done at the hip this energy was used to i increase the kinetic and potential energy of the arms at take off ii store and release energy from the muscles and tendons around the ankle knee and hip joint and iii pull on the body through an upward force acting on the trunk at the shoulder it was concluded that none of the prevailing theories exclusively explains the enhanced performance in the arm swing jump but rather the enhanced performance is based on several mechanisms operating together

fuzzy modeling with hybrid systems in this paper we present different approaches to the problem of fuzzy rules extraction by using a combination of fuzzy clustering and genetic algorithms as the main tools this combination of techniques let us define a hybrid system by which we can have different approaches in a fuzzy modeling process for example we can obtain a first approximation to the fuzzy rules that describe the system behavior represented by a collection of raw data without any assumption about the structure of the data using a fuzzy clustering technique and subsequently these rules can be tuned using a genetic algorithm alternatively this genetic algorithm can be used in order to generate and tune the fuzzy rules directly from the data with or without some priori information finally their performances are compared

recurrent neuro fuzzy hybrid learning approach to accurate system modeling a recurrent neuro fuzzy approach with ro lse hybrid learning algorithm to the problem of system modeling is proposed in the paper the proposed recurrent neuro fuzzy system possesses six layers of neural network to perform the fuzzy inference the recurrent structure is formed using lagged membership grade signals as internal feedbacks to the layer of membership functions of fuzzy sets and it is expected having great potential to trace the temporal change of signals fuzzy sets with time varying kernels have excellent property with that the input output mapping of the neuro fuzzy system is no longer fixed but time varying in this study a new parameter learning approach is proposed for nfs with good learning convergence in which the hybrid ro lse learning algorithm is utilized for the update of parameters the well known random optimization ro method is used to update the parameters of the premise parts of the proposed system and the method of least square estimation lse to update those of the consequent parts the hybrid algorithm is found useful and it has shown fast convergence of parameter learning for the proposed system three examples are used to demonstrate the brilliancy of the proposed approach excellent performance of the proposed approach in modeling accuracy and learning convergence is observed

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

neuro fuzzy systems for function approximation abstract we present a neuro fuzzy architecture for function approximation based on supervised learning the learning algorithm is able to determine the structure and the parameters of a fuzzy system the approach is an extension to our already published nefcon and nefclass models which are used for control or classification purposes the proposed extended model which we call nefprox is more general and can be used for any application based on function approximation

the simulation of aerial movement ii a mathematical inertia model of the human body a mathematical inertia model which permits the determination of personalized segmental inertia parameter values from anthropometric measurements is described the human body is modelled using 40 geometric solids which are specified by 95 anthropometric measurements a stadium solid is introduced for modelling the torso segments using perimeter and width measurements this procedure is more accurate than the use of elliptical discs of given width and depth and permits a smaller number of such solids to be used inertia parameter values may be obtained for body models of up to 20 segments errors in total body mass estimates from this and other models are discussed with reference to the unknown lung volumes

equilibria of recurrent fuzzy systems unlike static fuzzy systems recurrent fuzzy systems are equipped with feedback loops and thus exhibit dynamic behaviors the dynamics of a recurrent fuzzy system is largely determined by its rule base the dynamic behavior of a significant subclass of recurrent fuzzy systems may be immediately deduced from their rule base without need for analyzing their mathematical description their equilibrium points may be readily identified and their stability behaviors investigated based on their rule base the investigations involved lead to convergence theorems and other statements that preclude chaotic dynamics