Novel neural network model for simulating biological bidirectional cognition capability, and training method

Abstract

The invention discloses a novel neural network model for simulating biological bidirectional cognition capability, and a training method. The model consists of a positive neural network and a negative neural network. The positive neural network completes the simulation of a positive cognition process from the input to the output, and the negative neural network completes the simulation of a positive cognition process from the output to the input, wherein the two neural network structures are symmetric, and share a weight. The corresponding connection weight matrixes of the positive and negative neural networks are in a transposition relation. According to the invention, a coordinative structure of the positive and negative neural networks which are symmetric in structure and shares the weight is built, thereby achieving the simulation of the biological bidirectional cognition capability. A mode of negative learning process is introduced into a process of a standard BP algorithm, and a novel neural network training method is proposed.

Description

technical field [0001] The invention relates to the field of artificial neural networks, in particular to a novel neural network model and a training method for simulating biological bidirectional cognitive abilities. Background technique [0002] Neural network is an information processing system that simulates the structure and functions of the human brain. It is mainly composed of artificial neurons and network structures. The artificial neurons simulate the information processing process of biological neurons, and the network structure simulates the neurons in the biological nervous system. The connection mode, while the network connection weights and biases are responsible for memorizing the corresponding synaptic connection status. As an active marginal interdisciplinary subject, it is becoming a research hotspot in machine learning, artificial intelligence, cognitive science, neurophysiology, nonlinear dynamics and other related fields. [0003] (1) Neural network mo...

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Problems solved by technology

[0009] In order to solve the above problems, the present invention provides a novel neural network model and training method for simulating biological bidirectional cognitive ability, and establishes a mutual learning neural network model capable of simulating biological "bidirectional cognitive ability"; Mutual learning neural network training method for problems such as slow convergence speed and poor generalization ability of network training methods

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