The neural network control system has become a fuzzy control system. It is an emerging control method. Just as we say that Xiao Ming is very good at learning, how to make a good law is a very vague concept. The key to fuzzy control and classical control theory is that he has a set of his own fuzzy algorithm. This mathematical requirement is still quite high, and fuzzy control is also used as an intelligent control system.
Analysis of the characteristics of neural networks(1) General characteristics of neural networks
As a new type of technology neural network that is emerging has its own advantages, his main features are as follows:
1 Because neural networks mimic human brains, adaptive algorithms are employed. It makes it more adaptable to the changes of the environment than the fixed reasoning method of the expert system and the instruction program of the traditional computer. Summarize the rules and accomplish certain tasks, inferences, recognition, and control tasks. Therefore, it has a higher level of intelligence and is closer to the human brain.
2 Strong fault tolerance enables the neural network to identify objects based on the main features of the object, just like the artificial vision system. 3 Self-learning, self-organizing functions and inductive capabilities.
The above three characteristics are that the neural network can identify and process uncertain and unstructured information and images. A large amount of information in oil exploration has this property. Therefore, artificial neural networks are very suitable for information processing in petroleum exploration.
(2) Characteristics of self-organizing neural networks
Self-organizing feature map neural network as a kind of neural network, there are three main features of the neural network as described above and have their own characteristics.
1 Self-organizing neural networks are divided into two layers, the input layer and the output layer.
2 Using the competitive learning mechanism, the winner is king, but at the same time the neighbors also have the privilege to adjust the weights together with the winning neurons, thus making the results smoother and not as rough as before.
3 This network considers the topology problem at the same time, that is, he not only analyzes the input data itself, but also considers the topology of the data.
Both the weight adjustment process and the final result output take these into account, allowing similar neurons to be in adjacent positions, thereby realizing the function of different types of signals in response to brain partitioning similar to the human brain.
4 Using the unsupervised learning mechanism, without the teacher signal, the classification operation is directly performed, which makes the network more adaptable and widely used, especially the classification of data whose results are unknown to the present people. The tenacious vitality makes the application of neural networks greatly increased.
Advantages of self-organizing neural networks over traditional methods
The inherent characteristics of self-organizing feature map neural networks determine the advantages of neural networks over traditional methods:
(1) Self-organizing characteristics, reducing human intervention and reducing human modeling work. This is especially important for geophysical data processing where mathematical models are unclear, reducing the negative impact of inaccurate and even erroneous models on the results.
(2) The powerful adaptive ability greatly reduces the staff's programming work, so that the liberated processor has more energy to consider the impact of parameter adjustment on the results. Make faster improvements possible.
(3) Considering the topology of data and network in the process of network work, it is more similar to the way human brain thinks about problems. The solution of problems is more in line with human characteristics, and the credibility of the results is increased.
(4) There is no tutor learning mechanism and no teacher signal is required. This is an advantage for geophysical exploration, where there are few accurate teacher signals to guide, and it is a good reference to the human brain. The results are a good reference for other methods.
About artificial neural networks
Artificial neural network is a new artificial intelligence technology developed in recent years. He has a higher level than artificial intelligence technologies such as expert systems and fuzzy theory.
Artificial neural networks developed rapidly in the mid-1980s. In 1982, Prof. Hopfield, a physicist at California State Polytechnic Institute, proposed the Hopfield artificial neural network model. He introduced the concept of energy function into artificial neural network, and gave the criterion of stability. He developed artificial neural network for associative memory. And new ways to optimize calculations.
The ability of artificial neural networks to simulate part of human image thinking is a way to simulate artificial intelligence. In particular, artificial neural networks can be used to solve some of the problems encountered in artificial intelligence research. The application of artificial neural network theory has penetrated into many fields, and has made gratifying progress in computer vision, pattern recognition, intelligent control, nonlinear optimization, adaptive filtering phase information processing, and robotics.
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