Practical Stability Issues on Cmac Neural Networks

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STABILITY OF INVERTED PENDULUM USING NEURAL NETWORKS INTRODUCTION 1.1 Overview: 1.1.1 Overview of Neural Networks: Borrowing from biology, researchers are exploring neural networks—a new, nonalgorithmic approach to information processing. A Neural Network is a powerful data-modeling tool that is able to capture and represent complex input/output relationships. The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform "intelligent" tasks similar to those performed by the human brain. Neural networks resemble the human brain in the following two ways: 1. A neural network acquires knowledge through learning. 2. A neural network's knowledge is stored within inter-neuron connection strengths known as synaptic weights. Neural networks have shown great progress in identification of nonlinear systems. There are certain characteristics in ANN which assist them in identifying complex nonlinear systems. ANN are made up of many nonlinear elements and this gives them an advantage over linear techniques in modelling nonlinear systems. ANN are trained by adaptive learning, the network ‘learns’ how to do tasks, perform functions based on the data given for training. The knowledge learned during training is stored in the synaptic weights. The standard ANN structures (feedforward and recurrent) are both used to model the inverted pendulum. 1.1.1.1 Introduction to Learning: The main task of this project is to design a neural network controller which keeps the pendulum system stabilized. There are 3 main types of neural control – supervised, direct inverse and unsupervised. 1.1.1.1.1 Supervised Learning: Supervised learning uses an existing controller or human feedback in

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