In this paper we are studying the tolerance of Hopfield neural network for storage and recalling of fingerprint images. The feature extraction of these images is performed with FFT, DWT and SOM. These feature vectors are stored as associative memory in Hopfield Neural Network with Hebbian learning and Pseudoinverse learning rules. The objective of this study is to determine the optimal weight m...

This paper investigates the scaling properties of neural networks for solving job-shop scheduling problems. Specifically, the Tank-Hopfield linear programming network is modified to solve mixed integer linear programming with the addition of step-function amplifiers. Using a linear energy function, our approach avoids the traditional problems associated with most Hopfield networks using quadrat...

In this paper, we derive a sufficient condition for asymptotic stability of the zero solution of delay-difference control system of Hopfield neural networks in terms of certain matrix inequalities by using a discrete version of the Lyapunov second method. The result is applied to obtain new asymptotic stability condition for some class of delay-difference control system such as delay-difference...

In this work, a novel method, based upon Hopfield neural networks, is proposed for parameter estimation in the context of system identification. This subject is a very active field of research, because even when a model of a physical system is available, some parameters may be uncertain or time varying. In our methodology, identification is formulated as an optimization problem, profiting from ...

Recently, a large number of neural networks models have been proposed and studied extensively since Hopfield constructed a simplified neural network. In most networks however, it is usually expected that time delays exist during the processing and transmission of signals. In general, delay-differential equations exhibit much more complicated dynamics than ordinary differential equations since a...

In this paper, a new approach based on Artificial Neural Networks to solve the robot motion planning problem is presented. For this purpose, a Hopfield Neural Network is used in a certain constraint satisfaction problem of the robot motion planning in conjunction with fuzzy modeling of the real robot’s environment so that the energy of a state can be interpreted as the extent to which a hypothe...

A routing scheme that uses a neural network has been developed that can aid in establishing point-to-point communication routes through multistage interconnection networks (MINs). The neural network is a network of the type that was examined by Hopfield (Hopfield, 1984 and 1985). In this work, the problem of establishing routes through random MINs (RMINs) in a shared-memory, distributed computi...

It is well known that studies on neural dynamical systems not only involve discussion of stability property, but also involve other dynamics behaviors such as periodic oscillatory, bifurcation and chaos. In many applications, the property of periodic oscillatory solutions are of great interest. For example, the human brain has been in periodic oscillatory or chaos state, hence it is of prime im...