A fast modified constructive-covering algorithm for binary multi-layer neural networks

Wang, Di; Chaudhari, Narendra S.

doi:10.1016/j.neucom.2005.12.124

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…It was not easy to establish the model of complex production processes because there exists so much measurement noise that one pattern sample was labeled by at least one label. Similarly, the authors (Wang, 2008;Wang and Chaudhari, 2006) proposed a modified constructive-covering algorithm for binary NN. A separating hyperplane was obtained using geometrical distribution of pattern samples and the constructed separating hypersphere, and it was considered hidden unit.…”

Section: Introductionmentioning

confidence: 99%

An improved pattern-based prediction model for a class of industrial processes

Wang

Pan

2021

Transactions of the Institute of Measurement and Control

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For the problem of simplifying pattern-based modeling procedures, an improved pattern-based modeling method is put forward via pattern classification for a class of complex processes. It is a pure data-driven modeling method using statistical attributes of the processes. At the beginning of the paper, a method of system dynamics description based on pattern moving is introduced. Then, an improved method of pattern-moving-based prediction modeling is put forward, and it simplifies the pattern-moving-based modeling method by integrating an initial model and a classification mapping. It consists of two parts: system pattern construction and pattern classification. And a constructive classification neural network (CCNN) is designed to describe system dynamics by classifying the system pattern, and its generalization is discussed. Finally, simulations using data of an actual production process demonstrate the feasibility of the proposed modeling method, and the effectiveness of the CCNN is verified using comparison experiments.

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Section: Introductionmentioning

confidence: 99%

An improved pattern-based prediction model for a class of industrial processes

Wang

Pan

2021

Transactions of the Institute of Measurement and Control

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“…Sample classification, including linear and nonlinear sample classification, is the most basic problem in neural networks [1,2]. Linearly separable structures are employed in most classification cases due to the characteristic of being simple relatively.…”

Section: Introductionmentioning

confidence: 99%

The upper bound of the minimal number of hidden neurons for the parity problem in binary neural networks

Yang

Wang

Huang

2011

Sci. China Inf. Sci.

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Binary neural networks (BNNs) have important value in many application areas. They adopt linearly separable structures, which are simple and easy to implement by hardware. For a BNN with single hidden layer, the problem of how to determine the upper bound of the number of hidden neurons has not been solved well and truly. This paper defines a special structure called most isolated samples (MIS) in the Boolean space. We prove that at least 2 n−1 hidden neurons are needed to express the MIS logical relationship in the Boolean space if the hidden neurons of a BNN and its output neuron form a structure of AND/OR logic. Then the paper points out that the n-bit parity problem is just equivalent to the MIS structure. Furthermore, by proposing a new concept of restraining neuron and using it in the hidden layer, we can reduce the number of hidden neurons to n. This result explains the important role of restraining neurons in some cases. Finally, on the basis of Hamming sphere and SP function, both the restraining neuron and the n-bit parity problem are given a clear logical meaning, and can be described by a series of logical expressions.

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