Abstract
the neural network, fuzzy set theory and evolutionary algorithm in artificial intelligence are all intelligent information processing theories that follow the biological processing mode. These theories are realized by rational logical thinking mode without considering the role of human perceptual thinking in the information processing process, such as emotion and cognition. Among them, the neural network mainly imitates the function of the mental system of human, adopts the method from the bottom to the top, and processes the difficult language pattern information through a large number of complicated connections of neurons. Artificial neural network (Ann) is a cross research field of artificial intelligence and life science. This theory mainly imitates the information processing mechanism of organisms in nature and is mainly used in intelligent information processing systems that can adapt to long-term changes in the environment. Therefore, neural network has important application significance in the research of intelligence, robot and artificial emotion.
Highlights
Artificial neuron modelArtificial neuron model is based on the basic conditions for the creatures in the nature of information, with the need to connect neurons, as in the nature of biological information processing mechanism and connections between neurons network composition complex, its essence is through the simple artificial neuron densely connected guarantee have a certain number of real-valued output neurons, so as to realize input neurons units[1]
Abstract. the neural network, fuzzy set theory and evolutionary algorithm in artificial intelligence are all intelligent information processing theories that follow the biological processing mode
Artificial neuron model is based on the basic conditions for the creatures in the nature of information, with the need to connect neurons, as in the nature of biological information processing mechanism and connections between neurons network composition complex, its essence is through the simple artificial neuron densely connected guarantee have a certain number of real-valued output neurons, so as to realize input neurons units[1]
Summary
Artificial neuron model is based on the basic conditions for the creatures in the nature of information, with the need to connect neurons, as in the nature of biological information processing mechanism and connections between neurons network composition complex, its essence is through the simple artificial neuron densely connected guarantee have a certain number of real-valued output neurons, so as to realize input neurons units[1]. In figure 1, the inputoutput relationship of the artificial neuron model is. The input and output relationship of the artificial neuron model is as follows:
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
