Computational modeling of cerebellar model articulation controller Dissertation

Computational stylusling of cerebellar representative articulation controller (CMAC) and its application - Dissertation compositors caseIt will address simulations of the cerebellum and neural networks to accomplish biped zombi leg and control leg swing in environments with obstacles, in multi output, non-linear systems. According to Miller, Glanz, & Kraft, the cerebellar mode articulation controller (CMAC) can serve as a substitute method to back propagation (Miller, Glanz, & Kraft, 1990). The method includes a footstep planning system that is based on the Q-learning concept for biped robot control in dynamical environments. The effectiveness of major problem solving methods in control robot technology research is also of central focus. Predictable and unpredictable dynamic obstacles encountered in the system, such as memory usage, are discussed and a strategy to get well these obstacles is presented. The empirical analysis includes identification of likely Cerebellum Model A rticulation Controller (CMAC) problems in specific environments, inputs and outputs, and viable solutions. The aim of this research is to present a HCAQ-CMAC model that provides memory size and footstep planning solutions for the biped robot in a dynamic environment. Table of Contents ACKNOWLEDGEMENTS...2 ABSTRACT....3 Contents.4 List of Figures.6 List of Tables...9 Chapter 1 Overview..10 SECTION 1.1 Timeline of development..10 Section 1.2 The cerebellum...12 subsection 1.2.1 INPUTS....14 subsection 1.2.2 OUTPUTS....14 subsection 1.2.3 CEREBELLAR CORTEX....15 CHAPTER 2 Brain Computer larboard (BCI) INPUT AND OUTPUT....16 Section 2.1 Neural Networks ...19 Section 2.2 Q-Learning AND FUZZY CMAC......22 Chapter 3 theory.. 28 Section 3.1 The cerebellar mode articulation controller (CMAC). 28 Section 3.2 CMAC Hierarchically Clustered Adaptive Quantization.34 subsection 3.2.1 Mossy Fibers..36 Section 3.3 CMAC for design of Biped Robot 38 subsection 3.3.2 heuristics40 CHAPTER 4 fOOTSEP pLA NNING fUZZY q44 section 4.1 Control Strategy for obstacle Avoidance