The two well-known neural network, Hopfield networks and Radial Basis Function networks, have different structures and characteristics. Hopfield neural network and RBF neural network are two of the most commonly-used types of feedback networks and feedforward networks respectively. This study gives an overview for Hopfield neural network and RBF neural network in architectures, the learning pro...

This paper presents an improvement for an artificial neural network paradigm that has shown a significant potential for successful application to a class of optimization problems in structural engineering. The artificial neural network paradigm includes algorithms that belong to the class of single-layer, relaxationtype recurrent neural networks. The suggested improvement enhances the convergen...

In this paper a novel multi-user receiver is introduced, which unites fast convergence of neural networks with the asymptotically global optimization power of stochastic algorithms (e.g. Boltzmann machines). The proposed method is capable to achieve a 1..2 dB gain in performance over the traditional Hopfield neural network, while only 2 or 3 times more iterations is needed, which still does not...

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...

ecological changes resulting from climate conditions can severely affect human societies especially in the area of economy and safety. climate catastrophes may cause social and economic tension. forecasting such changes accurately can help the government to control the disasters and to achieve possible benefits (such as water supply in flood). weather forecasting is the application of science a...

this paper intends to offer a new iterative method based on articial neural networks for finding solution of a fuzzy equations system. our proposed fuzzied neural network is a ve-layer feedback neural network that corresponding connection weights to output layer are fuzzy numbers. this architecture of articial neural networks, can get a real input vector and calculates its corresponding fu...

Abstract − The asymptotic behavior of a class discrete-time Hopfield neural network is studied in this paper. Some properties for this class discrete-time neural network, such as the boundedness of motion trajectory, the uniqueness and the absolute stability of equilibrium point etc, are obtained. In this paper, the sufficient conditions related to the existence of unique equilibrium point and ...

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 ...

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...

Many difficult combinatorial optimization problems arising from science and technology are often difficult to solve exactly. Hence a great number of approximate algorithms for solving combinatorial opthintion problems have been developed [lo], [IS]. Hopfield and Tank applied the continuowtime, continuous-output Hopfield neural network (CTCGH?W) to TSP, thereby initialing a new approach to optim...