An overview of intelligent systems (neural networks) from the perspective of classical theory and their application in modeling and control of complex systems

In recent years, with the advancement of industry and technology, we see that systems are becoming more and more complex. This complexity in the industry has required a parallel progress in control systems (controllers), which has multiplied the need for advanced and intelligen t controllers. One of the most important criteria in the design of any control system is the accurate knowledge of the system or, more precisely, the modeling of the system. Considering these two challenges, this paper examines intelligent systems and specifically neural networks from the perspective of classical theory and their application in modeling and control of complex systems. At first, the basic element of neural networks, i.e. neuron, is introduced and its types of models (collective and radial) are also presented. Then the types of neural networks such as multilayer perceptron neural networks (MLP-NN), radial basis neural networks (RBF-NN) and recurrent neural networks (RNN) are described and the appropriate number of layers and also the appropriate number of neurons in the hidden layers in neural networks for different applications and especially for the approximation of nonlinear functions (modeling) are discussed. Then, it is tried to build a bridge between the concepts of neural networks (intelligent world) and the concepts of classical world and analyze neural networks from the perspective of classical theory. It is shown that from the point of view of classical theory, a neural network can be considered as a model structure and its weights and biases as unknown parameters of this structure. In neural networks, learning algorithms are used to determine the unknown parameters of the network (weights and biases). Therefore, learning algorithms of neural networks are studied from the point of view of classical theory and specifically in relation to numerical optimization and a bridge will be built between learning algorithms and numerical optimization methods. Also, the most important neural network learning algorithms are introduced along with their advantages and disadvantages. In the end, two important applications of neural networks, modeling and control, are discussed and for each of these applications, several illustrative examples are presented and simulated to show the effectiveness of neural networks.