The role of soft computing in intelligent machines

Area of Science:

• Computational Intelligence
• Artificial Intelligence
• Machine Learning

Background:

• Intelligent machines require computational intelligence for sophisticated behaviors.
• Knowledge representation and processing are fundamental to machine intelligence.
• Approximation capabilities are key manifestations of intelligence, crucial for tasks like pattern recognition and decision-making.

Purpose of the Study:

• To examine the application of soft computing in the development of intelligent machines.
• To explore the characteristics and capabilities of intelligent machines.
• To introduce approaches and a general structure for building intelligent machines.

Main Methods:

• Soft computing, integrating fuzzy logic, probability theory, neural networks, and genetic algorithms.
• Analysis of intelligent machine components: sensors, actuators, controllers, and communication backbone.
• Illustrating intelligent tuning within an industrial soft computing application.

Main Results:

• Soft computing synergistically combines techniques to emulate human-like reasoning.
• A general structure for intelligent machines is presented, emphasizing component interaction.
• An industrial example demonstrates the practical implementation of soft computing for intelligent tuning.

Conclusions:

• Soft computing is a vital component of computational intelligence for creating intelligent machines.
• Understanding machine structure and soft computing's role is crucial for development.
• Intelligent machines utilizing soft computing offer practical solutions, as shown by the industrial case study.