Finite Impulse Response Neural Networks with Applications in Speed Control of DC Motor Drive


An increasing number of applications of dynamic neural networks has been developed for digital signal processing (DSP), dynamic neural networks are feedforward neural networks with commonly used scalar synapses replaced by linear filters. This provides feedforward neural networks with the capability of performing dynamic mappings, which depend on past input values, dynamic neural networks are suitable for time series prediction, nonlinear system identification, and signal processing applications.

Their most popular types are Finite Impulse Response (FIR) neural networks, which are obtained by replacing synapses with finite impulse response filters. Due to their guaranteed stability characteristic and easy to minimize error surface they have been used with great success in many applications such as signal enhancement, noise cancellation, classification of input patterns, system identification, prediction, and control..

Most of the works on system identification using neural networks are based on multilayer feedforward neural networks with backpropagation learning or more efficient variations of this algorithm, an elegant method for training layered networks. This paper is based on work in a Dynamic System Modeling (DYSMO) and as an application for speed control of DC motor drive.

  • Abstract
  • 1. Introduction
  • 2. Model
  • 3. FIR Neural Network Model
  • 4. Training and Backpropagation
  • 5. Instantaneous Gradient Descent
  • 6. Temporal Backpropagation
  • 7. Learning Characteristics
  • 8. Training Characteristics
  • 9. Simulations
  • 10. Summaries
  • References

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