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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Complex Valued Deep Neural Networks for Nonlinear System Modeling.

Mario Lopez-Pacheco1, Wen Yu1

  • 1Departamento de Control Automático, CINVESTAV-IPN, Ciudad de México, Mexico.

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|September 28, 2021
PubMed
Summary
This summary is machine-generated.

Complex valued convolutional neural networks (CVCNN) effectively model nonlinear dynamic systems with missing data and noise. Novel training methods enhance CVCNN performance over traditional neural networks.

Keywords:
Complex valuedConvolutional neural networksSystem modeling

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Area of Science:

  • Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Deep learning, including convolutional neural networks (CNN), excels at pattern recognition and system identification.
  • Traditional CNNs struggle with dynamic system modeling when faced with missing data and significant noise.
  • Nonlinear systems with uncertainties present a challenge for existing deep learning approaches.

Purpose of the Study:

  • To introduce a complex valued convolutional neural network (CVCNN) for modeling nonlinear dynamic systems.
  • To develop and propose novel training methodologies tailored for CVCNN.
  • To evaluate the efficacy of CVCNN against established neural network models.

Main Methods:

  • Development of a complex valued convolutional neural network (CVCNN) architecture.
  • Implementation of innovative training algorithms specifically designed for CVCNN.
  • Comparative analysis of CVCNN performance against classical neural network models using benchmark datasets.

Main Results:

  • CVCNN demonstrates superior performance in modeling nonlinear dynamic systems characterized by missing data and high noise levels.
  • The proposed novel training methods significantly improve the accuracy and robustness of CVCNN.
  • Comparative studies confirm the advantages of CVCNN over traditional neural networks in challenging modeling scenarios.

Conclusions:

  • Complex valued convolutional neural networks (CVCNN) offer a robust solution for dynamic system modeling under conditions of uncertainty and data corruption.
  • The developed training methods are crucial for unlocking the full potential of CVCNN.
  • CVCNN represents a significant advancement for applications requiring reliable modeling of complex, noisy systems.