What Is Neural Network Architecture?

Neural networks were first proposed by psychologists and neurobiologists. Since neural networks can provide a relatively simple method for solving complex problems, they have attracted more and more attention in recent years. Neural Network Models Various and diverse models describe and simulate the biological nervous system at different levels from different perspectives. Representative network models include BP network, RBF network, Hopfield network, self-organizing feature map network, and so on.

Neural networks were first proposed by psychologists and neurobiologists. Since neural networks can provide a relatively simple method for solving complex problems, they have attracted more and more attention in recent years. Neural Network Models Various and diverse models describe and simulate the biological nervous system at different levels from different perspectives. Representative network models include BP network, RBF network, Hopfield network, self-organizing feature map network, and so on. Using these network models can achieve functions such as function approximation, data clustering, pattern classification, and optimization calculations. Therefore, neural networks are widely used in information processing in artificial intelligence, automatic control, robotics, statistics and other fields.

A classic neural network is a three-layer network. The red is the input layer, the green is the output layer, and the purple is the middle layer ( also called the hidden layer ) . The input layer has 3 input units, the hidden layer has 4 units, and the output layer has 2 units. as the picture shows.

Neural networks are an important machine learning technique. It is the basis of deep learning, which is currently the hottest research direction. Neural networks have broad and attractive prospects in the areas of system identification, pattern recognition, and intelligent control. Especially in intelligent control, people are particularly interested in the self-learning function of neural networks, and regard this important feature of neural networks as one of the key keys to solving the problem of adaptive capacity of controllers in automatic control. The foundation of neural networks is neurons. Neurons are biological models based on nerve cells of the biological nervous system. When people researched the biological nervous system to explore the mechanism of artificial intelligence, the neurons were mathematicalized, resulting in a mathematical model of neurons. A large number of neurons of the same form are connected together to form a neural network. Neural network is a highly nonlinear dynamic system. Although the structure and function of each neuron are not complicated, the dynamic behavior of neural networks is very complicated; therefore, neural networks can express various phenomena in the actual physical world. Neural network models are based on the mathematical models of neurons. The neural network model is represented by network topology, node characteristics, and learning rules.