Results 291 to 300 of about 124,788 (331)
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1997
This chapter introduces a technique for empirically testing feed-forward Neural Network architectures. The technique, Artificial Network Generation (ANG), makes possible a controlled series of experiments that statistically validates Occam’s Razor as a design methodology for network architectures in the context ofgradient descent learning algorithms ...
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This chapter introduces a technique for empirically testing feed-forward Neural Network architectures. The technique, Artificial Network Generation (ANG), makes possible a controlled series of experiments that statistically validates Occam’s Razor as a design methodology for network architectures in the context ofgradient descent learning algorithms ...
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Applications of Hybrid Fuzzy Neural Nets and Fuzzy Neural Nets
1998The two topics of this chapter are: build hybrid fuzzy neural nets to equal fuzzy expert systems, fuzzy input-output controllers, and to evaluate certain fuzzy functions; and (2) show how first training a fuzzy neural net can solve the overfitting problem mentioned in Chapter 3.
James J. Buckley, Thomas Feuring
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Biological Cybernetics, 1977
This paper deals with the problem of self-controlling nets of Caianiello's type. These nets control themselves through their own elements.
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This paper deals with the problem of self-controlling nets of Caianiello's type. These nets control themselves through their own elements.
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1993
The aim of this chapter is to give an overview of existing neural network simulators, their performance, and hardware requirements. The intention of Artificial Neural Network (ANN) Simulators is to provide the possibility of testing the performance of network types, architectures, initialisations, algorithms and parameter sets.
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The aim of this chapter is to give an overview of existing neural network simulators, their performance, and hardware requirements. The intention of Artificial Neural Network (ANN) Simulators is to provide the possibility of testing the performance of network types, architectures, initialisations, algorithms and parameter sets.
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1993
The term “neural networks” describes a class of models, which appear under different names in the literature: neural networks, neural computation, artificial neural systems, connectionist models, parallel distributed models. The aim of this chapter is to systematically examine and categorise neural networks according to their industrial application ...
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The term “neural networks” describes a class of models, which appear under different names in the literature: neural networks, neural computation, artificial neural systems, connectionist models, parallel distributed models. The aim of this chapter is to systematically examine and categorise neural networks according to their industrial application ...
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1988
The modeling of neural nets is a theoretical exercise whereby the investigator attempts either (1) to determine whether known or suspected signal processing or signal generating properties of an observed network of neurons are derivable from a specified set of primitive properties of the individual neurons and their connections; or (2) to determine the
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The modeling of neural nets is a theoretical exercise whereby the investigator attempts either (1) to determine whether known or suspected signal processing or signal generating properties of an observed network of neurons are derivable from a specified set of primitive properties of the individual neurons and their connections; or (2) to determine the
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A reticular chemistry guide for the design of periodic solids
Nature Reviews Materials, 2021Hao Jiang +2 more
exaly
Single-Layer Neural Net Competes with Multi-layer Neural Net
2008This paper presents a novel neural network with only one layer which can compete with multi-layer neural nets. This novel neural net is called a double-threshold single-layer neural net. The theoretical analysis and experiments show that it can demonstrate similar performance as multi-layer neural nets.
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