primal dual solution for the linear programming problems using neural networks in this paper we represent two new methods for the solution of canonical form linear programming problems in order to solve this linear programming problem we must minimize energy function of the corresponding neural network here energy function is considered as a liapunov function and we use treated hopfield neural network first new method finds optimal solution for primal problem using neural network while second new method composes primal and dual problem and therefore finds optimal solution for both problems numerical results compared with simplex solution and find that the convergence of two new methods to the correct solution is too fast even faster than neguyen s method the new methods are fully stable

on solving constrained optimization problems with neural networks a penalty method approach deals with the use of neural networks to solve linear and nonlinear programming problems the dynamics of these networks are analyzed in particular the dynamics of the canonical nonlinear programming circuit are analyzed the circuit is shown to be a gradient system that seeks to minimize an unconstrained energy function that can be viewed as a penalty method approximation of the original problem next the implementations that correspond to the dynamical canonical nonlinear programming circuit are examined it is shown that the energy function that the system seeks to minimize is different than that of the canonical circuit due to the saturation limits of op amps in the circuit it is also noted that this difference can cause the circuit to converge to a different state than the dynamical canonical circuit to remedy this problem a new circuit implementation is proposed

integral action in robust adaptive control conditions are studied under which integral action in robust adaptive control is soundly based the method of analysis amounts to verifying that the key conditions needed for convergence are satisfied a simple strategy is described for choosing the integral constant and global convergence is established for the resultant algorithm this illustrates a proof paradigm which could be similarly applied to other problems the principal assumptions used are that the plant when augmented by an integrator has a known degree of controllability and that an overbounding function is known for the unmodeled system response the continuous time case is treated but the corresponding discrete time results follow mutatis mutandis by simply replacing p d dt by delta q 1 delta l sub 2 by l sub 2 integral by sigma and so on

a novel key management scheme for dynamic access control in a user hierarchy considering the solution of dynamic access problems in a user hierarchy a novel scheme based on one way hash function is proposed to manage the cryptographic keys in the paper the scheme attempts to achieve both efficiency and non iteration in deriving the successor s secret key besides the other issues in relation with dynamic access control problems such as adding deleting classes adding deleting relationships and changing secret keys can be held good to the scheme in view of security a competent central authority must provide the user a convenient way to change his her key at any time therefore the design toward the algorithm in the paper contains such a function what weight to mention especially among these characteristics of the scheme is the simplification of procedure in changing the private key and no other current keys need altering simultaneously

an unconstrained convex programming approach to linear semi infinite programming in this paper an unconstrained convex programming dual approach for solving a class of linear semi infinite programming problems is proposed both primal and dual convergence results are established under some basic assumptions numerical examples are also included to illustrate this approach

numerical solution for linear and quadratic programming problems using a recurrent neural network a recurrent neural network is proposed in this paper for solving linear and quadratic programming problems the main advantage of this network is that here we are not in need of parameters setting moreover using this network we can solve primal programming problems and their duals simultaneously we prove the global convergence of the neural network and demonstrate the advanced performance of the proposed network by means of simulation of several numerical examples

a robust indirect adaptive control approach this paper considers the robust design of an indirect adaptive control approach which is applicable when the unknown parameters of a linear time invariant plant lie in a known convex set throughout which no unstable pole zero cancellation occurs in order to achieve the robustness the use of a relative dead zone in the adaptive law is proposed it is shown that with a suitably designed relative dead zone the adaptive control system is globally stable even in the presence of small unmodeled plant uncertainties

access control in user hierarchy based on elliptic curve cryptosystem this work proposes a novel key management method based on elliptic curve cryptosystem and one way hash function to solve dynamic access problems in a user hierarchy the proposed scheme attempts to derive the secret key of successors efficiently and non redundantly it includes functions such as insertion and removal of classes updating of their relationships and changing of secret keys the method utilizes a central authority which enables a user to change the secret key at will conveniently since the proposed method uses the elliptic curve cryptosystem which has a low computational cost and small key size its performance in terms of both security and efficiency is quite commendable therefore it can be anticipated that its use will be extended to wireless communication in the future

numerical solutions for constrained quadratic problems using high performance neural networks two new classes of neural networks for solving constrained quadratic programming problems are presented the main advantage of these networks is the requirement to use economic analog multipliers for variables the numerical simulations demonstrate that in the new neural networks not only the cost of the hardware implementation is not relatively expensive but also accuracy of the solution is greatly good the network dynamic behaviors are discussed the numerical simulations are shown that an optimal solution of the quadratic problems is an equilibrium point of the neural dynamics and vise versa we show that these networks find the solution of both primal and dual problems and converge to the corresponding exact solutions globally the proposed new neural networks models are fully stable

solving quadratic programming problems by delayed projection neural network in this letter the delayed projection neural network for solving convex quadratic programming problems is proposed the neural network is proved to be globally exponentially stable and can converge to an optimal solution of the optimization problem three examples show the effectiveness of the proposed network

robust model reference adaptive control we propose a new model reference adaptive control algorithm and show that it provides the robust stability of the resulting closed loop adaptive control system with respect to unmodeled plant uncertainties the robustness is achieved by using a relative error signal in combination with a dead zone and a projection in the adaptive law the extra a priori information needed to design the adaptive law are bounds on the plant parameters and an exponential bound on the impulse response of the inverse plant transfer function

an efficient key assignment scheme for access control in a large leaf class hierarchy the employees of an organization are usually divided into different security classes to authorize the information retrieval and the number of leaf classes is substantially larger than the number of non leaf classes additionally the alternations in leaf classes are more frequent than in non leaf classes we proposed a new key assignment scheme for controlling the access right in a large poset partially ordered set hierarchy to reduce the required computation for key generation and derivation with the storage amount of data decreased

linear and quadratic programming neural network analysis neural networks for linear and quadratic programming are analyzed the network proposed by m p kennedy and l o chua ieee trans circuits syst vol 35 pp 554 562 may 1988 is justified from the viewpoint of optimization theory and the technique is extended to solve optimization problems such as the least squares problem for quadratic programming the network converges either to an equilibrium or to an exact solution depending on whether the problem has constraints or not the results also suggest an analytical approach to solve the linear system bx b without calculating the matrix inverse the results are directly applicable to optimization problems with c sup 2 convex objective functions and linear constraints the dynamics and applicability of the networks are demonstrated by simulation the distance between the equilibria of the networks and the problem solutions can be controlled by the appropriate choice of a network parameter

neurocomputing with time delay analysis for solving convex quadratic programming problems this paper presents a neural network computational scheme with time delay consideration for solving convex quadratic programming problems based on some known results a delay margin is explicitly determined for the stability of the neural dynamics under which the states of the neural network does not oscillate the configuration of the proposed neural network is provided operational characteristics of the neural network are demonstrated via numerical examples

solving convex programming problems with equality constraints by neural networks abstract this paper presents a neural network approach for solving convex programming problems with equality constraints after defining the energy function and neural dynamics of the proposed neural network we show the existence of an equilibrium point at which the neural dynamics becomes asymptotically stable it is shown that under proper conditions an optimal solution of the underlying convex programming problems is an equilibrium point of the neural dynamics and vise versa the configuration of the proposed neural network with an exact layout is provided for solving linear programming problems the operational characteristics of the neural network are demonstrated by numerical examples

nonlinear optimization using generalized hopfield networks a nonlinear neural framework called the generalized hopfield network ghn is proposed which is able to solve in a parallel distributed manner systems of nonlinear equations the method is applied to the general nonlinear optimization problem we demonstrate ghns implementing the three most important optimization algorithms namely the augmented lagrangian generalized reduced gradient and successive quadratic programming methods the study results in a dynamic view of the optimization problem and offers a straightforward model for the parallelization of the optimization computations thus significantly extending the practical limits of problems that can be formulated as an optimization problem and that can gain from the introduction of nonlinearities in their structure e g pattern recognition supervised learning and design of content addressable memories

a two phase optimization neural network a novel two phase neural network that is suitable for solving a large class of constrained or unconstrained optimization problem is presented for both types of problems with solutions lying in the interior of the feasible regions the phase one structure of the network alone is sufficient when the solutions of constrained problems are on the boundary of the feasible regions the proposed two phase network is capable of achieving the exact solutions in contrast to existing optimization neural networks which can obtain only approximate solutions furthermore the network automatically provides the corresponding lagrange multiplier associated with each constraint thus for linear programming the network solves both the primal problems and their dual problems simultaneously

tracking and disturbance rejection of mimo nonlinear systems with pi controller we study tracking and disturbance rejection of a class of mimo nonlinear systems with linear proportional plus integral pi compensator roughly speaking we show that if the given nonlinear plant is exponentially stable and has a strictly increasing dc steady state i o map then a simple pi compensator can be used to yield a stable unity feedback closed loop system which asymptotically tracks reference inputs that tend to constant vectors and asymptotically rejects disturbances that tend to constant vectors

design issues in adaptive control an integrated approach to the design of practical adaptive control algorithms is presented many existing ideas are brought together and the effect of various design parameters available to a user is explored the theory is extended by showing how the problem of stabilizability of the estimated model can be overcome by running parallel estimators it is shown how asymptotic tracking of deterministic set points can be achieved in the presence of unmodeled dynamics

an optimal algorithm for assigning cryptographic keys to control access in a hierarchy a cryptographic scheme for controlling access to information within a group of users organized in a hierarchy was proposed in 1 the scheme enables a user at some level to compute from his own cryptographic key the keys of the users below him in the organization

convergence analysis of a differential equation approach for solving nonlinear programming problems we propose a system of differential equations to find a kuhn tucker point of a nonlinear programming problem with both equality and inequality constraints it is proven that the kuhn tucker point of the nonlinear programming problem is an asymptotically stable equilibrium point of the proposed differential system an iterate algorithm is constructed to find an equilibrium point of the differential system the global convergence and local quadratic convergence rate of this algorithm are demonstrated and illustrative examples are given

existence and stability of equilibria of the continuous time hopfield neural network the qualitative properties of the continuous time hopfield neural network are investigated by using the cauchy existence theorem of ordinary differential equations a set of new and simple sufficient conditions guaranteeing the existence and uniqueness of equilibrium is derived furthermore global and asymptotical stability conditions are established by means of lyapunov method

a secure effective key management scheme for dynamic access control in a large leaf class hierarchy lo et al 2011 proposed an efficient key assignment scheme for access control in a large leaf class hierarchy where the alternations in leaf classes are more frequent than in non leaf classes in the hierarchy their scheme is based on the public key cryptosystem and hash function where operations like modular exponentiations are very much costly compared to symmetric key encryptions and decryptions and hash computations their scheme performs better than the previously proposed schemes however in this paper we show that lo et al s scheme fails to preserve the forward security property where a security class c x can also derive the secret keys of its successor classes c j s even after deleting the security class c x from the hierarchy we aim to propose a new key management scheme for dynamic access control in a large leaf class hierarchy which makes use of symmetric key cryptosystem and one way hash function we show that our scheme requires significantly less storage and computational overheads as compared to lo et al s scheme and other related schemes through the informal and formal security analysis we further show that our scheme is secure against all possible attacks including the forward security in addition our scheme supports efficiently dynamic access control problems compared to lo et al s scheme and other related schemes thus higher security along with low storage and computational costs make our scheme more suitable for practical applications compared to other schemes

solving complementarity and variational inequalities problems using neural networks in this paper we propose a recurrent neural network model for solving a class of monotone variational inequalities problem with linear constraints the neural network is stable in the sense of lyapunov and globally convergent to an optimal solution compared with the existing convergence results the present proof do not require lipschitz continuity condition on the objective function this neural network model has no adjustable parameter thus its structure is very simple variational inequalities problem with general set of constraints plus a general form of the complementarity problems are solved using the proposed neural networks some examples demonstrated to show the applicability of the proposed neural networks to solve various nonlinear optimization problems numerically