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Stochastic Hopfield neural networks
, 2003
"... Hopfield (1984 Proc. Natl Acad. Sci. USA 81 3088–92) showed that the time evolution of a symmetric neural network is a motion in state space that seeks out minima in the system energy (i.e. the limit set of the system). In practice, aneuralnetwork is often subject to environmental noise. It is there ..."
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Hopfield (1984 Proc. Natl Acad. Sci. USA 81 3088–92) showed that the time evolution of a symmetric neural network is a motion in state space that seeks out minima in the system energy (i.e. the limit set of the system). In practice, aneuralnetwork is often subject to environmental noise
Hopfield Neural Networks—A Survey
"... Abstract:- In this work we survey the Hopfield neural network, introduction of which rekindled interest in the neural networks through the work of Hopfield and others. Hopfield net has many interesting features, applications, and implementations and it comes in two flavors, digital and analog. A bri ..."
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Cited by 1 (0 self)
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Abstract:- In this work we survey the Hopfield neural network, introduction of which rekindled interest in the neural networks through the work of Hopfield and others. Hopfield net has many interesting features, applications, and implementations and it comes in two flavors, digital and analog. A
Absolute Stability of Hopfield Neural Network
"... Abstract. This paper presents some new results for the absolute stability of Hopfield neural networks with activation functions chosen from sigmoidal functions which have unbounded derivatives. Detailed discussions are also given to the relation and difference of absolute stabilities between neural ..."
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Abstract. This paper presents some new results for the absolute stability of Hopfield neural networks with activation functions chosen from sigmoidal functions which have unbounded derivatives. Detailed discussions are also given to the relation and difference of absolute stabilities between neural
Hopfield Neural Network with Glial Network
"... A glia is a nervous cell existing in a brain. This cell has im-portant functions for the higher brain function. We have pro-posed a glial network for an artificial network from functions of the biological glia. In this study, we propose a Hopfield Neural Network (Hopfield NN) with glial network. In ..."
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A glia is a nervous cell existing in a brain. This cell has im-portant functions for the higher brain function. We have pro-posed a glial network for an artificial network from functions of the biological glia. In this study, we propose a Hopfield Neural Network (Hopfield NN) with glial network
Opinion Dynamics with Hopfield Neural Networks
, 712
"... In Hopfield neural networks with up to 10 8 nodes we store two patterns through Hebb couplings. Then we start with a third random pattern which is supposed to evolve into one of the two stored patterns, simulating the cognitive process of associative memory leading to one of two possible opinions. W ..."
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In Hopfield neural networks with up to 10 8 nodes we store two patterns through Hebb couplings. Then we start with a third random pattern which is supposed to evolve into one of the two stored patterns, simulating the cognitive process of associative memory leading to one of two possible opinions
Hopfield Neural Networks for Vector Precoding
"... Abstract—We investigate the application of Hopfield neural networks (HNN) for vector precoding in wireless multiple-input multiple-output (MIMO) systems. We apply the HNN to vector precoding with N transmit and K receive antennas, and obtain simulation results for the average transmit energy optimiz ..."
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Abstract—We investigate the application of Hopfield neural networks (HNN) for vector precoding in wireless multiple-input multiple-output (MIMO) systems. We apply the HNN to vector precoding with N transmit and K receive antennas, and obtain simulation results for the average transmit energy
GRAY BOX IDENTIFICATION WITH HOPFIELD NEURAL NETWORKS
"... 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 ..."
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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
Intuitionistic fuzzy Hopfield neural network and its stability
"... Intuitionistic fuzzy sets (IFSs) are generalization of fuzzy sets by adding an additional attribute parameter called non-membership degree. In this paper, a max-min intuitionistic fuzzy Hopfield neural network (IFHNN) is proposed by combining IFSs with Hopfield neural networks. The stability of IFHN ..."
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Cited by 2 (0 self)
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Intuitionistic fuzzy sets (IFSs) are generalization of fuzzy sets by adding an additional attribute parameter called non-membership degree. In this paper, a max-min intuitionistic fuzzy Hopfield neural network (IFHNN) is proposed by combining IFSs with Hopfield neural networks. The stability
Applying Hopfield Neural Networks for Artificial Intelligence problems
, 1995
"... Artificial Intelligence (AI) is known to be rich with problems, where finding the solution by conventional search methods is computationally intensive. The time required is often exponential to the number of variables. Principly different way of searching for the decision is to build a dynamic self- ..."
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-organizing system, where the stable states correspond to the desirable solutions. In the paper the possibility of using such nondeterministic methods for resolving Artificial Intelligence problems is studied. It is shown why Hopfield Neural Networks (HN) are so suitable for the role of such a dynamical system
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