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Intelligent Systems and Control

The intelligent control techniques use artificial intelligence computing approaches like neural networks, fuzzy logic, evolutionary computation, genetic algorithms and Bayesian probability. Artificial intelligence deals with intelligence of the machines and the branch of computer science that cerates it.

The major sub-domains of intelligent control systems are:

1. Neural Network Control- Neural network traditionally meant a circuit of network of biological neurons. In modern terms it refers to the artificial neural networks which comprise of artificial neurons or nodes. There are two types of neural networks are biological and artificial. The biological network comprises of real biological neurons that interconnect functionally to form the central nervous system or the peripheral nervous system. The artificial neural networks are made up of digital neurons which are interconnected.

2. Bayesian Control- The Bayesian probability and control belongs to the concept of evidential probabilities and control. The Bayesian probability is an extension of logic which enables reasoning with uncertain statements. The Particle filter and the Kalman filter are the two examples of popular Bayesian control components. The controller design Bayesian approach requires an important effort in deriving the measurement and system model. These are the mathematical relationships which link the state variables to the measurements which are available in the controlled systems. It is very closely related to system theoretic approach of control design in this respect.

3. Fuzzy (logic) Control- It deals with approximate reasoning rather than abstract and is a form of many valued logic. The extended form of fuzzy logic also handles the concept of partial truth and value of truth ranges from completely true to completely false.

4. Neuron Fuzzy Control- The combination of fuzzy logic and artificial neural networks is termed as neuro fuzzy logic and control. The combination forms a hybrid intelligent system which synergizes the two techniques and combines the human like reasoning style of a fuzzy system with the neural networks which are learning and connectionist structure.

5. Expert Systems-The software that uses the human expertise knowledge base for problem solving or clarifying uncertainties is called an expert system. One or more than one human experts are consulted in the process which uses a wide variety of methods to stimulate and expert’s performance. The expert system is a subfield of artificial intelligence.

6. Artificial Agents- An autonomous entity which observes and acts upon an environment and then directs its activities towards achieving certain goals is called and autonomous agent. As they are intelligence agents, they can learn the knowledge given for achieving their goals. They can be simple as well as complex.

New models and techniques of intelligent behavior are created and new computational methods are being developed for supporting them.

The Neural Network Controllers

Neural networks are used for problem solving in almost all fields of problem solving. The neural network control has basically two steps which are the system identification and control. A feed forward network which has non linear, differentiable and continuous activation functions has universal approximation capabilities. Some recurrent networks are also used for identifying systems. System identification forms mapping among the input-output data pairs. A network of this type captures a system dynamics.

The most basic problem of intelligent system is what to do next. This is characterized by the action selection process. The action selection problem in artificial intelligence and computational cognitive science is associated with animates and intelligent agents. These are the artificial systems exhibiting complex behaviors in agent environment. These terms are also used in animal behavior sciences.