Hierarchically Clustered Adaptive Quantization CMAC and Its Learning Convergence

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Active disturbance cancellation in nonlinear dynamical systems using neural networks

A proposal for the use of a time delay CMAC neural network for disturbance cancellation in nonlinear dynamical systems is presented. Appropriate modifications to the CMAC training algorithm are derived which allow convergent adaptation for a variety of secondary signal paths. Analytical bounds on the maximum learning gain are presented which guarantee convergence of the algorithm and provide insight into the necessary reduction in learning gain as a function of the system parameters. Effectiveness of the algorithm is evaluated through mathematical analysis, simulation studies, and experimental application of the technique on an acoustic duct laboratory model

Stability and weight smoothing in CMAC neural networks

Although the CMAC (Cerebellar Model Articulation Controller) neural network has been successfully used in control systems for many years, its property of local generalization, the availability of trained information for network responses at adjacent untrained locations, although responsible for the networks rapid learning and efficient implementation, results in network responses that is, when trained with sparse or widely spaced training data, spiky in nature even when the underlying function being learned is quite smooth. Since the derivative of such a network response can vary widely, the CMAC\u27s usefulness for solving optimization problems as well as for certain other control system applications can be severely limited. This dissertation presents the CMAC algorithm in sufficient detail to explore its strengths and weaknesses. Its properties of information generalization and storage are discussed and comparisons are made with other neural network algorithms and with other adaptive control algorithms. A synopsis of the development of the fields of neural networks and adaptive control is included to lend historical perspective. A stability analysis of the CMAC algorithm for open-loop function learning is developed. This stability analysis casts the function learning problem as a unique implementation of the model reference structure and develops a Lyapunov function to prove convergence of the CMAC to the target model. A new CMAC learning rule is developed by treating the CMAC as a set of simultaneous equations in a constrained optimization problem and making appropriate choices for the weight penalty matrix in the cost equation. This dissertation then presents a new CMAC learning algorithm which has the property of weight smoothing to improve generalization, function approximation in partially trained networks and the partial derivatives of learned functions. This new learning algorithm is significant in that it derives from an optimum solution and demonstrates a dramatic performance improvement for function learning in the presence of widely spaced training data. Developed from a completely unique analytical direction, this algorithm represents a coupling and extension of single- and multi-resolution CMAC algorithms developed by other researchers. The insights derived from the analysis of the optimum solution and the resulting new learning rules are discussed and suggestions for future work are presented

Pole -mounted sonar vibration prediction using CMAC neural networks

The efficiency and accuracy of pole-mounted sonar systems are severely affected by pole vibration, Traditional signal processing techniques are not appropriate for the pole vibration problem due to the nonlinearity of the pole vibration and the lack of a priori knowledge about the statistics of the data to be processed. A novel approach of predicting the pole-mounted sonar vibration using CMAC neural networks is presented. The feasibility of this approach is studied in theory, evaluated by simulation and verified with a real-time laboratory prototype, Analytical bounds of the learning rate of a CMAC neural network are derived which guarantee convergence of the weight vector in the mean. Both simulation and experimental results indicate the CMAC neural network is an effective tool for this vibration prediction problem

Efficient techniques for soft tissue modeling and simulation

Performing realistic deformation simulations in real time is a challenging problem in computer graphics. Among numerous proposed methods including Finite Element Modeling and ChainMail, we have implemented a mass spring system because of its acceptable accuracy and speed. Mass spring systems have, however, some drawbacks such as, the determination of simulation coefficients with their iterative nature. Given the correct parameters, mass spring systems can accurately simulate tissue deformations but choosing parameters that capture nonlinear deformation behavior is extremely difficult. Since most of the applications require a large number of elements i. e. points and springs in the modeling process it is extremely difficult to reach realtime performance with an iterative method. We have developed a new parameter identification method based on neural networks. The structure of the mass spring system is modified and neural networks are integrated into this structure. The input space consists of changes in spring lengths and velocities while a "teacher" signal is chosen as the total spring force, which is expressed in terms of positional changes and applied external forces. Neural networks are trained to learn nonlinear tissue characteristics represented by spring stiffness and damping in the mass spring algorithm. The learning algorithm is further enhanced by an adaptive learning rate, developed particularly for mass spring systems. In order to avoid the iterative approach in deformation simulations we have developed a new deformation algorithm. This algorithm defines the relationships between points and springs and specifies a set of rules on spring movements and deformations. These rules result in a deformation surface, which is called the search space. The deformation algorithm then finds the deformed points and springs in the search space with the help of the defined rules. The algorithm also sets rules on each element i. e. triangle or tetrahedron so that they do not pass through each other. The new algorithm is considerably faster than the original mass spring systems algorithm and provides an opportunity for various deformation applications. We have used mass spring systems and the developed method in the simulation of craniofacial surgery. For this purpose, a patient-specific head model was generated from MRI medical data by applying medical image processing tools such as, filtering, the segmentation and polygonal representation of such model is obtained using a surface generation algorithm. Prism volume elements are generated between the skin and bone surfaces so that different tissue layers are included to the head model. Both methods produce plausible results verified by surgeons

Analysis and Comparison of the Structure and Performance of Local Neural Networks

The paper synthesizes the local neural networks. Network structures and their activation functions of three local networks CMAC, B-spline, RBF that are often used to approach functions are analyzed and compared in detail. The network structure of ART-2 is also discussed. Based on the fuzzy system of these local networks, the paper depicts their fuzzy structures and performances. The study and analysis in the paper are useful to instruct to select and design the local neural networks