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Soft Computing in Industrial Applications

:	António Gaspar-Cunha, Ricardo Takahashi, Gerald Schaefer, Lino Costa
:	Soft Computing in Industrial Applications
:	Springer-Verlag
:	9783642205057
:	Advances in Intelligent and Soft Computing
:	1
:	CHF 189.50
:

:	Allgemeines, Lexika
:	English

:	438
:	Wasserzeichen
:	PC/MAC/eReader/Tablet
:	PDF

The 15th Online World Conference on Soft Computing in Industrial Applications, held on the Internet, constitutes a distinctive opportunity to present and discuss high quality papers, making use of sophisticated Internet tools and without incurring in high cost and, thus, facilitating the participation of people from the entire world.

The book contains a collection of papers covering outstanding research and developments in the field of Soft Computing including, evolutionary computation, fuzzy control and neuro-fuzzy systems, bio-inspired systems, optimization techniques and application of Soft Computing techniques in modeling, control, optimization, data mining, pattern recognition and traffic and transportation systems.

	Title	2
	Organization	8
	Contents	18
	Plenary Sessions	23
	An Introduction to Multi-Objective Particle Swarm Optimizers	24
	Introduction	24
	Basic Concepts	25
	An Introduction to Particle Swarm Optimization	26
	Particle Swarm Optimization for Multi-Objective Problems	29
	Future Research Paths	32
	Conclusions	32
	References	32
	Direct Load Control in the Perspective of an Electricity Retailer – A Multi-Objective Evolutionary Approach	34
	Introduction	34
	A Multi-Objective Model for the Design of Load Control Actions	37
	A Case Study and Illustrative Results	39
	Conclusions	45
	References	46
	Tutorial	48
	Evolutionary Approaches for Optimisation Problems	49
	Evolutionary Computing	49
	Systems	50
	Objective Function	50
	Search Space and Fitness Landscape	50
	Optimisation	51
	Optimisation Loop	52
	Genetic Algorithms	53
	Selection	55
	Cross-Over	57
	Mutation	58
	Discussion	58
	Schemata Theorem	59
	Coding Problem	60
	Genetic Programming	62
	Selection	62
	Cross-Over	63
	Mutation	63
	Ant Colony Optimisation	64
	Particle Swarm Optimisation	69
	Conclusions	71
	References	71
	Part I: Evolutionary Computation	73
	Approaches for Handling Premature Convergence in CFG Induction Using GA	74
	Introduction	74
	Methodologies Adapted	76
	Elite Mating Pool (EMP) Approach	77
	Dynamic Application of Reproduction Operator (DARO)	78
	The Language Set Used	79
	Experimental Setup and Outcome	79
	Conclusion	83
	References	84
	A Novel Magnetic Update Operator for Quantum Evolutionary Algorithms	86
	Introduction	86
	QEA	87
	QEA Structure	88
	Quantum Gates Assignment	88
	Magnetic Update Operator	89
	Parameter Tuning	92
	Experimental Results	92
	Conclusion	94
	References	95
	Improved Population-Based Incremental Learning in Continuous Spaces	96
	Introduction	96
	PBIL Algorithms	97
	Performance Testing	101
	Comparative Results	102
	Conclusions and Discussion	104
	References	105
	Particle Swarm Optimization in the EDAs Framework	106
	Introduction and Related Work	106
	Particle Swarm Optimization	107
	Estimation of Distribution Algorithms	109
	Particle Swarm Estimation of Distribution Algorithm	110
	Experiments	112
	Conclusion and Future Work	114
	References	115
	Differential Evolution Based Bi-Level Programming Algorithm for Computing Normalized Nash Equilibrium	116
	Introduction	116
	Nash Equilibrium and the GNEP	117
	Nikaido Isoda Function	118
	Solution Approaches for the GNEP	118
	A Bi-Level Programming Approach for GNEPs	118
	Differential Evolution for Bi-Level Programming	119
	Numerical Examples	121
	Problem 1	121
	Problem 2	121
	Problem 3	122
	Problem 4	122
	Problems 5a and 5b	122
	Results	123
	Conclusions	124
	References	124
	Part II: Fuzzy Control and Neuro-Fuzzy Systems	126
	Estimating CO Conversion Values in the Fischer-Tropsch Synthesis Using LoLiMoT Algorithm	127
	Introduction	127
	Experimental Studies	129
	Catalyst Preparation	129
	Catalyst Testing	129
	Kinetic Experimental Data	130
	Modeling Study	130
	Local Linear Neuro-Fuzzy Network	130
	Locally Linear Model Tree	130
	Results and Discussion	132
	Conclusions	135
	References	135
	Global Optimization Using Space-Filling Curves and Measure-Preserving Transformations	138
	Introduction	138
	Auxiliary Theoretical Results	140
	Fuzzy Adaptive Simulated Annealing	143
	Proposed Algorithm	144
	Experiments	145
	Conclusions	146
	References	147
	Modelling Copper Omega Type Coriolis Mass Flow Sensor with an Aid of ANFIS Tool	148
	Introduction	148
	Adaptive Network Based Fuzzy Inference System (ANFIS)	150
	ANFIS/Neural Network Modeling of Phase Shift	151
	Experimental Test Conditions	152
	Results and Discussions	153
	Conclusion	156
	References	157