声明
ABSTRACT
摘要
CONTENTS
ACKNOWLEGEMENT
CHAPTER 1 lNTRODUCTION
1.1 Background of complex adaptive system
1.1.1 Neural network learning
1.1.2 Scale free network
1.1.3 Small world network
1.1.4 Underground mining and wireless sensor networks examined
1.1.5 Routing in wireless sensor networks,signal reach and sensor deployment
1.2 The concept and design
1.2.1 Objective
1.2.2 Significance
1.2.3 Proposed model
1.3 Procedure for generation of the model
1.3.1 Generation of the routing path
1.3.2 Operation of particle swarm optimization
1.3.3 Operation of genetic algorithm
1.4 Main Works and Novelty
CHAPTER 2 LITERATURE REVIEW
2.1 Introduction
2.2 Sigmoid basis function
2.3 Radial basis function neural network
2.4 Hybrid Models or algorithms
2.5 Method
2.5.1 Particle swarm optimization(PSO)
2.5.2 Genetic algorithm
CHAPTER 3 ROUTING TOPOLOGY,NEW COMPACT RADIAL AND SIGMOID NEURAL NETWORKS
3.1 Introduction
3.2 Related work
3.3 Proposed routing topology
3.3.1 Deployment of sensors and connection
3.3.2 Communication and transmission range
3.3.3 Fault tolerant considerations
3.3.4 Hardware and software considerations
3.4 Simulation results and discussion
3.4.1 Sensor sequence and routing
3.4.2 Impact of explosion on transmission
3.4.3 Re-routing
3.5 The new compact radial and sigmoid neural networks
3.5.1 Introduction
3.5.2 Gaussian RBF
3.5.3 Gaussian with different power parameter
3.5.4 Proposed compact radial basis function(CRBF)
3.5.5 Evaluation of the fitness function
3.6 Simulation parameters,results and discussion
3.6.1 Parameters for simulation
3.6.2 Results and discussion
3.7 Conclusion
CHAPTER 4 4 WEIGHTED LINEAR AND NONLINEAR HYBRIDS NEURAL NETWORKS IN UNDERGROUND RESCUE MISSION
4.1 Introduction
4.2 Related work
4.3 The proposed weighted linear hybrid of sigmoid and compact radial functions
4.3.1 Simulation results and discussion
4.4 Weighted nonlinear hybrid neural networks of compact sigmoid and radial functions
4.4.1 Proposed nonlinear hybrid
4.4.2 Results and discussion
4.5 G-ratio weighted nonlinear hybrid neural networks
4.5.1 Simulation results and Discussion
4.6 Conclusion
CHAPTER 5 NEW COMPACT RADIAL BASIS FUNCTION WITH GENETIC ALGORITHM
5.1 Introduction
5.2 Related work
5.2.1 Limitations
5.3 Proposed Compact hybrid model based on Genetic Algorithm
5.4 Simulation results and discussion
5.4.1 The generated matrices
5.4.2 Training results
5.4.3 Performance of parameters of the various hybrids
5.4.4 General performance and computational efficiency
5.5 Conclusion
CHAPTER 6 CONCLUSION AND FUTURE WORK
REFERENCE
LIST OF PUBLICATION