A model of neurons with CHN (Continuous Hysteresis Neurons) for the Hopfield neural networks is studied. We prove theoretically that the emergent collective properties of the original Hopfield neural networks also are present in the Hopfield neural networks with continuous hysteresis neurons. The network architecture is applied to the N-Queens problem and results of computer simulations are pre...

The deterministic limit of a Hopfield-type mutation-selection model in the sequence space approach is investigated. Genotypes are identified with two-letter sequences. Mutation is modelled as a Markov process, fitness functions are of Hopfield type, where the fitness of a sequence is determined by the Hamming distances to a number of predefined patterns. Using a maximum principle for the popula...

In this research paper, the problem of existence of the associative memory synthesized by Hopfield is addressed and solved. Using Hadamard matrix of suitable dimension, an algorithm to synthesize real valued Hopfield neural network is discussed. The problem of existence and synthesis of a certain complex Hopfield neural network is addressed and solved. Also, synthesis of real and complex Hopfie...

We summarize the Storkey Learning Rules for the Hopfield Model, and evaluate performance relative to other learning rules. Hopfield Models are normally used for auto-association, and Storkey Learning Rules have been found to have good balance between local learning and capacity. In this paper we outline different learning rules and summarise capacity results. Hopfield networks are related to Bo...

This paper presents an improved approach for enhancing the performance of doing logic programming in Hopfield neural network. Generally Hopfield networks are suitable for solving combinatorial optimization problems. In spite of usefulness of Hopfield neural networks they have limitations; one of the most concerning drawbacks is that sometimes the solutions are local minimum instead of global mi...

This paper proposes an augmented Lagrange Hopfield network (ALHN) for combined heat and power economic dispatch (CHPED) problem. The ALHN method is the continuous Hopfield neural network with its energy function based on augmented Lagrangian relaxation. In the proposed ALHN, the energy function is augmented by Hopfield terms from Hopfield neural network and penalty factors from augmented Lagran...

This paper considers the use of discrete Hopfield neural networks for solving school timetabling problems. Two alternative formulations are provided for the problem: a standard Hopfield-Tank approach, and a more compact formulation which allows the Hopfield network to be competitive with swapping heuristics. It is demonstrated how these formulations can lead to different results. The Hopfield n...

After the original work of Hopfield and Tank, a lot of modified Hopfield neural network models have been proposed for combinatorial optimization problems. Recently, a positively selffeedbacked Hopfield neural network architecture was proposed by Li et al. and successfully applied to crossbar switching problem. In this paper, we analysis the dynamics of the positively self-feedbacked Hopfield ne...

This paper presents a theoretical analysis on the asymptotic memory capacity of the generalized Hopfield network. The perceptron learning scheme is proposed to store sample patterns as the stable states in a generalized Hopfield network. We have obtained that …n 2 1† and 2n are a lower and an upper bound of the asymptotic memory capacity of the network of n neurons, respectively, which shows th...

This paper presents an improved technique for accelerating the process of doing logic programming in discrete Hopfield neural network by integrating fuzzy logic and modifying activation function. Generally Hopfield networks are suitable for solving combinatorial optimization problems and pattern recognition problems. However Hopfield neural networks also face some limitations; one of the major ...