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Andina D, Ropero-Peláez FJ. On the biological plausibility of artificial metaplasticity learning algorithm. Neurocomputing 2013. [DOI: 10.1016/j.neucom.2012.09.028] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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THEODORIDIS DIMITRIOS, BOUTALIS YIANNIS, CHRISTODOULOU MANOLIS. INDIRECT ADAPTIVE CONTROL OF UNKNOWN MULTI VARIABLE NONLINEAR SYSTEMS WITH PARAMETRIC AND DYNAMIC UNCERTAINTIES USING A NEW NEURO-FUZZY SYSTEM DESCRIPTION. Int J Neural Syst 2012; 20:129-48. [DOI: 10.1142/s0129065710002310] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The indirect adaptive regulation of unknown nonlinear dynamical systems with multiple inputs and states (MIMS) under the presence of dynamic and parameter uncertainties, is considered in this paper. The method is based on a new neuro-fuzzy dynamical systems description, which uses the fuzzy partitioning of an underlying fuzzy systems outputs and high order neural networks (HONN's) associated with the centers of these partitions. Every high order neural network approximates a group of fuzzy rules associated with each center. The indirect regulation is achieved by first identifying the system around the current operation point, and then using its parameters to device the control law. Weight updating laws for the involved HONN's are provided, which guarantee that, under the presence of both parameter and dynamic uncertainties, both the identification error and the system states reach zero, while keeping all signals in the closed loop bounded. The control signal is constructed to be valid for both square and non square systems by using a pseudoinverse, in Moore-Penrose sense. The existence of the control signal is always assured by employing a novel method of parameter hopping instead of the conventional projection method. The applicability is tested on well known benchmarks.
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Affiliation(s)
- DIMITRIOS THEODORIDIS
- Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
| | - YIANNIS BOUTALIS
- Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
- Department of Electrical, Electronic and Communication Engineering, Chair of Automatic Control, University of Erlangen-Nuremberg, 91058 Erlangen, Germany
| | - MANOLIS CHRISTODOULOU
- Department of Electronic and Computer Engineering, Technical University of Crete, 73100 Chania, Crete, Greece
- Dipartimento di Automatica et Informatica, Politecnico di Torino, 10129 Torino, Italia
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AHMED SULTANUDDIN, SHAHJAHAN MD, MURASE KAZUYUKI. A LEMPEL-ZIV COMPLEXITY-BASED NEURAL NETWORK PRUNING ALGORITHM. Int J Neural Syst 2011; 21:427-41. [DOI: 10.1142/s0129065711002936] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This paper presents a pruning method for artificial neural networks (ANNs) based on the 'Lempel-Ziv complexity' (LZC) measure. We call this method the 'silent pruning algorithm' (SPA). The term 'silent' is used in the sense that SPA prunes ANNs without causing much disturbance during the network training. SPA prunes hidden units during the training process according to their ranks computed from LZC. LZC extracts the number of unique patterns in a time sequence obtained from the output of a hidden unit and a smaller value of LZC indicates higher redundancy of a hidden unit. SPA has a great resemblance to biological brains since it encourages higher complexity during the training process. SPA is similar to, yet different from, existing pruning algorithms. The algorithm has been tested on a number of challenging benchmark problems in machine learning, including cancer, diabetes, heart, card, iris, glass, thyroid, and hepatitis problems. We compared SPA with other pruning algorithms and we found that SPA is better than the 'random deletion algorithm' (RDA) which prunes hidden units randomly. Our experimental results show that SPA can simplify ANNs with good generalization ability.
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Affiliation(s)
- SULTAN UDDIN AHMED
- Department of Electronics and Communication Engineering, Khulna University of Engineering and Technology, Khulna-9203, Bangladesh
| | - MD. SHAHJAHAN
- Department of Electrical and Electronic Engineering, Khulna University of Engineering and Technology, Khulna-9203, Bangladesh
| | - KAZUYUKI MURASE
- Department of Human and Artificial Intelligence Systems, University of Fukui, Bunkyo 3-9-1, Fukui-910-8705, Japan
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A probabilistic neural network for earthquake magnitude prediction. Neural Netw 2009; 22:1018-24. [DOI: 10.1016/j.neunet.2009.05.003] [Citation(s) in RCA: 220] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2008] [Revised: 04/29/2009] [Accepted: 05/13/2009] [Indexed: 11/22/2022]
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