Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks in Computer Intelligence
Neural Networks in Computer Intelligence
Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities
Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities
Neuro-Fuzzy Applications in Telecommunications (Signals and Communication Technology)
Neuro-Fuzzy Applications in Telecommunications (Signals and Communication Technology)
Programming the Cell Broadband Engine Architecture: Examples and Best Practices
Programming the Cell Broadband Engine Architecture: Examples and Best Practices
Programming the Cell Processor: For Games, Graphics, and Computation
Programming the Cell Processor: For Games, Graphics, and Computation
Practical Computing on the Cell Broadband Engine
Practical Computing on the Cell Broadband Engine
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Neural networks offer various possibilities for function approximation. When provided a set of data points, the network learns to approximate the underlying function that generates those points. Although the network can be very efficient, the amount computation needed during the learning process can be very high. In order to improve this process, we explore the parallelization for the random scanning of starting points selected for the gradient descent algorithm using Cell-BE multiprocessor environment. We show the application of this method for approximating 3D nonlinear function, as well as for predicting 2D time series. We show that the parallel tracing of gradient descent trajectories of the 3D function approximation allows identifying a suitable starting condition for implementing an efficient gradient descent, while being able deliver the required accuracy of approximation in a shorter time. In 2D time series prediction the attained advantage is the possibility to achieve simultaneous prediction for various numbers of steps ahead. It is shown how the Cell-BE multiprocessor offers a convenient parallel environment for the above solutions.