Computational modeling of cerebellar model articulation controller Dissertation

Computational modeling of cerebellar model vox controller (CMAC) and its application - talk ExampleIt will address simulations of the cerebellum and neural networks to accomplish biped robot wooden 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 dish out as a substitute method to back propagation (Miller, Glanz, & Kraft, 1990). The method includes a footstep preparedness strategy that is based on the Q-learning concept for biped robot control in dynamical environments. The metier of major problem solving methods in control robot technology research is also of key focus. Predictable and unpredictable dynamic obstacles encountered in the system, such as memory usage, are discussed and a strategy to overcome these obstacles is presented. The empirical analysis includes identification of likely Cerebellum Model Articulation Controller (CMAC) p roblems in detail 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 preparedness solutions for the biped robot in a dynamic environment. Table of Contents ACKNOWLEDGEMENTS...2 ABSTRACT....3 Contents.4 harken of Figures.6 List of Tables...9 Chapter 1 Overview..10 SECTION 1.1 Timeline of development..10 instalment 1.2 The cerebellum...12 section 1.2.1 INPUTS....14 subsection 1.2.2 OUTPUTS....14 subsection 1.2.3 CEREBELLAR CORTEX....15 CHAPTER 2 Brain Computer Interface (BCI) INPUT AND OUTPUT....16 Section 2.1 flighty 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 pLANNING fUZZY q44 sect ion 4.1 Control Strategy for obstacle Avoidance