An Experimental Study on Nonlinear Function Computation for Neural/Fuzzy Hardware Design

An experimental study on the influence of the computation of basic nodal nonlinear functions on the performance of (NFSs) is described in this paper. Systems' architecture size, their approximation capability, and the smoothness of provided mappings are used as performance indexes for this comp...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2007-01, Vol.18 (1), p.266-283
Main Authors: Basterretxea, K., Tarela, J.M., del Campo, I., Bosque, G.
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Algorithms
Applied sciences
Approximation algorithms
Approximation capability
Artificial intelligence
Artificial neural networks
Backpropagation algorithms
centered recursive interpolation (CRI)
Computational modeling
Computer architecture
Computer science
control theory
systems
Computer-Aided Design
Connectionism. Neural networks
Electronics
Equipment Design
Equipment Failure Analysis
Exact sciences and technology
Fuzzy Logic
Fuzzy systems
Gaussian function
Hardware
Kernel
Neural networks
Neural Networks (Computer)
neurofuzzy hardware
Nonlinear Dynamics
Performance analysis
sigmoid function
Signal Processing, Computer-Assisted - instrumentation
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Summary:An experimental study on the influence of the computation of basic nodal nonlinear functions on the performance of (NFSs) is described in this paper. Systems' architecture size, their approximation capability, and the smoothness of provided mappings are used as performance indexes for this comparative paper. Two widely used kernel functions, the sigmoid-logistic function and the Gaussian function, are analyzed by their computation through an accuracy-controllable approximation algorithm designed for hardware implementation. Two artificial neural network (ANN) paradigms are selected for the analysis: backpropagation neural networks (BPNNs) with one hidden layer and radial basis function (RBF) networks. Extensive simulation of simple benchmark approximation problems is used in order to achieve generalizable conclusions. For the performance analysis of fuzzy systems, a functional equivalence theorem is used to extend obtained results to fuzzy inference systems (FISs). Finally, the adaptive neurofuzzy inference system (ANFIS) paradigm is used to observe the behavior of neurofuzzy systems with learning capabilities
ISSN:1045-9227
2162-237X
1941-0093
2162-2388
DOI:10.1109/TNN.2006.884680