Neural Network Algorithms and Their Engineering Applications

Front Cover
Neural Network Algorithms and Their Engineering Applications
Copyright
Contents
Preface
1 Introduction
	1.1 The development and classification of metaheuristic algorithms
	1.2 Overview and organization of the book
	References
2 Neural network algorithms
	2.1 Motivation
	2.2 Structure and implementation
	2.3 Challenges in engineering problems with multimodal properties
	Appendix 2.A Source code of NNA
	Appendix 2.B Source code of GSA
	Appendix 2.C Source code of TLBO
	References
3 Constrained engineering design optimization problems
	3.1 Problem statement
		3.1.1 Rolling element bearing design problem
		3.1.2 Speed-reducer design problem
		3.1.3 Pressure-vessel design problem
	3.2 Chaotic neural network algorithm with competitive learning
		3.2.1 Motivation of CCLNNA
		3.2.2 The framework and implementation of CCLNNA
	3.3 CCLNNA for real-world engineering design problems
		3.3.1 CCLNNA for the rolling element bearing design problem
		3.3.2 CCLNNA for the speed-reducer design problem
		3.3.3 CCLNNA for the pressure-vessel design problem
	3.4 Discussion on the validity of chaos theory and competitive learning
	Appendix 3.A Source code of CCLNNA
	Appendix 3.B Source code of GWO
	Appendix 3.C Source code of WOA
	Appendix 3.D Source code of SCA
	Appendix 3.E Source code of MVO
	Appendix 3.F Source code of HHO
	Appendix 3.G Source code of MFO
	Appendix 3.H Source code of HGSO
	Appendix 3.I Source code of SHO
	References
4 Parameter estimation of proton exchange membrane fuel cell models
	4.1 Problem statement
		4.1.1 Basic knowledge
		4.1.2 Mathematical formulation
		4.1.3 Objective function
	4.2 Multiple learning neural network algorithm
		4.2.1 Motivation
		4.2.2 The framework of MLNNA
	4.3 Application in the parameter estimation of PEMFC models
		4.3.1 Experimental results on the BCW 500 W PEMFC model
		4.3.2 Experimental results on the NedStack PS6 PEMFC model
	4.4 Discussion on the effectiveness of the improved strategies
	Appendix 4.A Source code of MLNNA
	Appendix 4.B Source code of SOA
	Appendix 4.C Source code of TSO
	Appendix 4.D Source code of BSA
	Appendix 4.E Source code of JAYA
	Appendix 4.F Source code of STOA
	References
5 Parameter extraction of photovoltaic models
	5.1 Problem statement
		5.1.1 Solar photovoltaic modeling
			5.1.1.1 Single-diode model
			5.1.1.2 Double-diode model
			5.1.1.3 PV module modeling
		5.1.2 Problem formulation
	5.2 TLNNA
		5.2.1 Teaching–learning-based optimization
			5.2.1.1 Teacher phase
			5.2.1.2 Learner phase
		5.2.2 The frame of TLNNA
		5.2.3 The implementation of TLNNA
	5.3 TLNNA for unconstrained benchmark functions
		5.3.1 Benchmark test functions
		5.3.2 Parameter settings
		5.3.3 Results and analysis
			5.3.3.1 The comparison of mean value and standard deviation
			5.3.3.2 The statistical results of tied rank
	5.4 Application in the parameter extraction of PV models
		5.4.1 Experimental results for SDM
		5.4.2 Experimental results for DDM
		5.4.3 Experimental results for PVM
	5.5 Discussion on the effectiveness of the hybrid strategy
	Appendix 5.A Source code of TLBO
	Appendix 5.B Source code of PGJAYA
	Appendix 5.C Source code of MLBSA
	Appendix 5.D Source code of SSA
	Appendix 5.E Source code of PSO
	References
6 Energy consumption optimization of a UAV-assisted IoT data collection system
	6.1 Problem statement
	6.2 The proposed method
		6.2.1 Basic knowledge
		6.2.2 Adaptive neural network algorithm
	6.3 Results and discussion
	Appendix 6.A Source code of DEVIPS
	Appendix 6.B Source code of DEEM
	Appendix 6.C Source code of DE
	References
7 Conclusion
	7.1 Content review
	7.2 Future direction
	Appendix 7.A Source code of SRLBSA
	References
Acronyms
Index
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