Results 141 to 150 of about 122,388 (186)
Some of the next articles are maybe not open access.
1998
The objective of this chapter is to show how to construct hybrid neural nets (HNN) to be computationally identical to discrete fuzzy expert systems (discussed in Chapter 4) and certain fuzzy controllers. An example of a 2 — 3 — 1 HNN is shown in Figure 5.1. Figure 5.1 is similar to Figure 3.1, however, the output y will be computed differently.
James J. Buckley, Thomas Feuring
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The objective of this chapter is to show how to construct hybrid neural nets (HNN) to be computationally identical to discrete fuzzy expert systems (discussed in Chapter 4) and certain fuzzy controllers. An example of a 2 — 3 — 1 HNN is shown in Figure 5.1. Figure 5.1 is similar to Figure 3.1, however, the output y will be computed differently.
James J. Buckley, Thomas Feuring
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Survival analysis and neural nets
Statistics in Medicine, 1994AbstractWe consider feed‐forward neural nets and their relation to regression models for survival data. We show how the back‐propagation algorithm may be used to obtain maximum likelihood estimates in certain standard regression models for survival data, as well as in various generalizations of these.
Liestøl, K. +2 more
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Communications of the ACM, 2019
Yoshua Bengio, Geoffrey Hinton, and Yann LeCun this month will receive the 2018 ACM A.M. Turing Award for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.
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Yoshua Bengio, Geoffrey Hinton, and Yann LeCun this month will receive the 2018 ACM A.M. Turing Award for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.
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1998
This chapter, and the next three chapters, are a fuzzification of Chapters 3, 4 and 5. In this chapter we fuzzify neural nets (Chapter 3). In the next chapter we fuzzify the first approximation results (Chapter 4) into the second approximation results. Hybrid neural netsh (Chapter 5) are fuzzified into hybrid fuzzy neural nets in Chapters 9 and 10.
James J. Buckley, Thomas Feuring
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This chapter, and the next three chapters, are a fuzzification of Chapters 3, 4 and 5. In this chapter we fuzzify neural nets (Chapter 3). In the next chapter we fuzzify the first approximation results (Chapter 4) into the second approximation results. Hybrid neural netsh (Chapter 5) are fuzzified into hybrid fuzzy neural nets in Chapters 9 and 10.
James J. Buckley, Thomas Feuring
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2001
Conventional neural networks work by changing the synaptical weights between their neurons. New neural nets (NNN) are presented, using the recording of temporal sequences of activity, generated by various patterns in chains of neurons, to store and reproduce those patterns.
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Conventional neural networks work by changing the synaptical weights between their neurons. New neural nets (NNN) are presented, using the recording of temporal sequences of activity, generated by various patterns in chains of neurons, to store and reproduce those patterns.
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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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