After decades of development, neural network theory has achieved widespread success in many research fields such as pattern recognition, automatic control, signal processing, assisted decision-making, and artificial intelligence. The following introduces the current application status of neural networks in some fields. In dealing with many problems, the information sources are incomplete and contain illusions, and the decision-making rules are sometimes contradictory and sometimes unorganized. This brings great difficulties to traditional information processing methods, but neural networks can work very well. Handle these issues and provide reasonable identification and judgment.
1. Information processing
The problems to be solved by modern information processing are very complex. Artificial neural networks can imitate or replace functions related to human thinking, and can realize automatic diagnosis, Problem solving, solving problems that cannot or are difficult to solve with traditional methods. The artificial neural network system has high fault tolerance, robustness and self-organization. Even if the connection line is highly damaged, it can still be in an optimized working state. This is widely used in military system electronic equipment. application. Existing intelligent information systems include intelligent instruments, automatic tracking and monitoring instrument systems, automatic control and guidance systems, automatic fault diagnosis and alarm systems, etc.
2. Pattern recognition
Pattern recognition is the processing and analysis of various forms of information that represent things or phenomena to describe, identify, classify and explain things or phenomena. process. This technology is based on Bayesian probability theory and Shannon's information theory, and its information processing process is closer to the logical thinking process of the human brain. There are two basic pattern recognition methods, namely statistical pattern recognition method and structural pattern recognition method. Artificial neural network is a commonly used method in pattern recognition. The artificial neural network pattern recognition method developed in recent years has gradually replaced the traditional pattern recognition method. After years of research and development, pattern recognition has become a relatively advanced technology and is widely used in text recognition, speech recognition, fingerprint recognition, remote sensing image recognition, face recognition, handwritten character recognition, industrial fault detection, precision guidance, etc. aspect. Due to the complexity and unpredictability of the human body and diseases, it is necessary to detect and express signals, and analyze the obtained data and information in terms of the manifestations and change patterns of biological signals and information (self-changes and changes after medical intervention). There are very complex nonlinear connections in many aspects such as decision-making and decision-making, which are suitable for the application of artificial neural networks. Current research involves almost all aspects from basic medicine to clinical medicine, and is mainly used in the detection and automatic analysis of biological signals, medical expert systems, etc.
1. Detection and analysis of biological signals
Most medical testing equipment outputs data in the form of continuous waveforms, and these waveforms are the basis for diagnosis. Artificial neural network is an adaptive dynamic system connected by a large number of simple processing units. It has functions such as massive parallelism, distributed storage, and self-organization of adaptive learning. It can be used to solve biomedical signal analysis and processing. Problems that are difficult or impossible to solve using conventional methods. The application of neural networks in biomedical signal detection and processing mainly focuses on the analysis of brain electrical signals, the extraction of auditory evoked potential signals, the identification of myoelectric and gastrointestinal electrical signals, the compression of electrocardiographic signals, and the recognition of medical images. and processing etc.
2. Medical expert system
Traditional expert systems store the experience and knowledge of experts in the form of rules in the computer, establish a knowledge base, and use logical reasoning. Medical diagnosis. However, in practical applications, as the size of the database increases, it will lead to an "explosion" of knowledge, and there will also be a "bottleneck" problem in the way of acquiring knowledge, resulting in very low work efficiency. Neural networks based on nonlinear parallel processing have pointed out a new development direction for the research of expert systems, solved the above problems of expert systems, and improved the reasoning, self-organization, and self-learning capabilities of knowledge, so that neural networks have played an important role in medical experts. The system has been widely used and developed.
Research in related fields such as anesthesia and critical care medicine involves the analysis and prediction of multiple physiological variables. There are some relationships and phenomena that have not yet been discovered or have no definite evidence in clinical data, signal processing, and automatic discrimination and detection of interference signals. , prediction of various clinical conditions, etc., can be applied to artificial neural network technology. 1. Market Price Forecast
The analysis of commodity price changes can be attributed to a comprehensive analysis of many factors that affect market supply and demand. Due to its inherent limitations, traditional statistical economics methods are difficult to make scientific predictions on price changes, while artificial neural networks can easily handle incomplete, fuzzy and uncertain data, or data with unclear regularity, so artificial neural networks are used for prediction. Price prediction has advantages that traditional methods cannot compare with. Starting from the market price determination mechanism, a more accurate and reliable model is established based on complex and changeable factors such as the number of households, per capita disposable income, loan interest rates, and urbanization levels that affect commodity prices. This model can scientifically predict the changing trend of commodity prices and obtain accurate and objective evaluation results.
2. Risk assessment
Risk refers to the economic or financial loss, natural damage or loss caused by the uncertainty in the process of engaging in a specific activity. Possibility of injury. The best way to prevent risks is to make scientific predictions and assessments of risks in advance. The prediction idea of ??applying artificial neural network is to construct the structure and algorithm of the credit risk model suitable for the actual situation based on the specific realistic risk sources, obtain the risk evaluation coefficient, and then determine the solution to the actual problem. Empirical analysis using this model can make up for the shortcomings of subjective evaluation and achieve satisfactory results. Since the formation of the neural network model, it has been inextricably linked to psychology. Neural networks are abstracted from the information processing functions of neurons, and the training of neural networks reflects cognitive processes such as perception, memory, and learning. Through continuous research, people are changing the structural model and learning rules of artificial neural networks, and exploring the cognitive functions of neural networks from different angles, laying a solid foundation for its research in psychology. In recent years, artificial neural network models have become an indispensable tool for exploring the mechanisms of advanced psychological processes such as social cognition, memory, and learning. Artificial neural network models can also study cognitive deficits in brain-injured patients, posing challenges to traditional cognitive positioning mechanisms.
Although artificial neural networks have made certain progress, there are still many shortcomings, such as: the application range is not broad enough and the results are not accurate enough; the training speed of the existing model algorithms is not high enough; the integration of the algorithm is not high enough. Not high enough; at the same time, we hope to find new breakthrough points in theory and establish new general models and algorithms. Further research on biological neuron systems is needed to continuously enrich people's understanding of human brain nerves.