Continuous-time recurrent neural networks for quadratic programming: theory and engineering applications.

• Other Authors: Liu, Shubao.
• Format: Others
• Language: English; Chinese
• Published: 2005
• Subjects: Neural networks (Computer science); Quadratic programming
• Online Access: http://library.cuhk.edu.hk/record=b5892519; http://repository.lib.cuhk.edu.hk/en/item/cuhk-325254

Liu Shubao. === Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. === Includes bibliographical references (leaves 90-98). === Abstracts in English and Chinese. === Abstract --- p.i === 摘要 --- p.iii === Acknowledgement --- p.iv === Chapter 1 --- Introduction --- p.1 === Chapter 1.1 --- Time-Varying Quadratic Optimization --- p.1 === Chapter 1.2 --- Recurrent Neural Networks --- p.3 === Chapter 1.2.1 --- From Feedforward to Recurrent Networks --- p.3 === Chapter 1.2.2 --- Computational Power and Complexity --- p.6 === Chapter 1.2.3 --- Implementation Issues --- p.7 === Chapter 1.3 --- Thesis Organization --- p.9 === Chapter I --- Theory and Models --- p.11 === Chapter 2 --- Linearly Constrained QP --- p.13 === Chapter 2.1 --- Model Description --- p.14 === Chapter 2.2 --- Convergence Analysis --- p.17 === Chapter 3 --- Quadratically Constrained QP --- p.26 === Chapter 3.1 --- Problem Formulation --- p.26 === Chapter 3.2 --- Model Description --- p.27 === Chapter 3.2.1 --- Model 1 (Dual Model) --- p.28 === Chapter 3.2.2 --- Model 2 (Improved Dual Model) --- p.28 === Chapter II --- Engineering Applications --- p.29 === Chapter 4 --- KWTA Network Circuit Design --- p.31 === Chapter 4.1 --- Introduction --- p.31 === Chapter 4.2 --- Equivalent Reformulation --- p.32 === Chapter 4.3 --- KWTA Network Model --- p.36 === Chapter 4.4 --- Simulation Results --- p.40 === Chapter 4.5 --- Conclusions --- p.40 === Chapter 5 --- Dynamic Control of Manipulators --- p.43 === Chapter 5.1 --- Introduction --- p.43 === Chapter 5.2 --- Problem Formulation --- p.44 === Chapter 5.3 --- Simplified Dual Neural Network --- p.47 === Chapter 5.4 --- Simulation Results --- p.51 === Chapter 5.5 --- Concluding Remarks --- p.55 === Chapter 6 --- Robot Arm Obstacle Avoidance --- p.56 === Chapter 6.1 --- Introduction --- p.56 === Chapter 6.2 --- Obstacle Avoidance Scheme --- p.58 === Chapter 6.2.1 --- Equality Constrained Formulation --- p.58 === Chapter 6.2.2 --- Inequality Constrained Formulation --- p.60 === Chapter 6.3 --- Simplified Dual Neural Network Model --- p.64 === Chapter 6.3.1 --- Existing Approaches --- p.64 === Chapter 6.3.2 --- Model Derivation --- p.65 === Chapter 6.3.3 --- Convergence Analysis --- p.67 === Chapter 6.3.4 --- Model Comparision --- p.69 === Chapter 6.4 --- Simulation Results --- p.70 === Chapter 6.5 --- Concluding Remarks --- p.71 === Chapter 7 --- Multiuser Detection --- p.77 === Chapter 7.1 --- Introduction --- p.77 === Chapter 7.2 --- Problem Formulation --- p.78 === Chapter 7.3 --- Neural Network Architecture --- p.82 === Chapter 7.4 --- Simulation Results --- p.84 === Chapter 8 --- Conclusions and Future Works --- p.88 === Chapter 8.1 --- Concluding Remarks --- p.88 === Chapter 8.2 --- Future Prospects --- p.88 === Bibliography --- p.89