Neural networks can refer to two types: one is the biological neural network, and the other is the artificial neural network. The biological neural network generally refers to the network composed of neurons, cells, and synapses in the biological brain, which is used to generate biological consciousness and assist organisms in thinking and acting. The artificial neural network (Artificial Neural Networks, abbreviated as ANNs) is also referred to as neural networks (NNs) or connection models. It is a mathematical model that mimics the behavioral characteristics of animal neural networks to perform distributed parallel information processing. This network relies on the complexity of the system, adjusting the interconnections among numerous internal nodes to achieve the goal of processing information. The artificial neural network is a mathematical model that processes information using a structure similar to the synaptic connections of the brain. In engineering and academia, it is often directly referred to as “neural networks” or neural-like networks.
Mathematical modeling is often used in the following aspects: Establishing models based on research of biological prototypes, creating theoretical models of neurons and neural networks, including conceptual models, knowledge models, physical-chemical models, and mathematical models. Algorithms are constructed based on theoretical model research to create specific neural network models for computer simulation or hardware preparation, including research on network learning algorithms. This work is also known as technical model research.The algorithms used in neural networks are vector multiplication and widely employ symbolic functions and various approximations. Parallelism, fault tolerance, hardware implementability, and self-learning characteristics are several basic advantages of neural networks, distinguishing their computational methods from traditional methods.Based on research of network models and algorithms, practical application systems are created using artificial neural networks, such as completing certain signal processing or pattern recognition functions, constructing expert systems, creating robots, and controlling complex systems, among others.Throughout the development history of contemporary emerging science and technology, humanity has traversed a rugged path in conquering the fields of outer space, fundamental particles, and the origin of life. We also see that research exploring brain functions and neural networks will evolve rapidly as we overcome numerous difficulties.Click the link below to download the attachment for study!
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