Primary direction neural network system

A neural network and main direction technology, applied in the field of main direction neural network system, can solve problems such as BP network convergence slow performance function, noise sensitivity, etc., achieve good approximation performance, excellent anti-noise performance, and overcome sensitivity

Inactive Publication Date: 2009-09-30
INST OF SEMICONDUCTORS - CHINESE ACAD OF SCI
View PDF0 Cites 13 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But they all have disadvantages, for example, the BP network converges slowly and is easy to fall into the local minimum of the performance function; the RBF network is more sensitive to noise

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Primary direction neural network system
  • Primary direction neural network system
  • Primary direction neural network system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0030] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0031] The main direction neural network system provided by the present invention is built on the basis of the density of a certain function set in the continuous function space, which provides a theoretical guarantee for the approximation performance of the network. The mapping approximation problem of neural network is divided into two stages. The first stage is to realize the natural clustering of samples in the input space, and the second stage is to solve the mapping based on the coverage model. The first stage is obtained by unsupervised methods, and the second stage is obtained by supervised learning. The neural network system not only has a clear structure, but also the algorithm is easy to understand and has good...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a primary direction neural network system with a four-layer feedforward structure. The primary direction neural network system comprises an input layer, a first hidden layer, a second hidden layer and an output layer, wherein the input layer comprises D neurons, the first hidden layer comprises K groups of neurons, each group of the neurons comprises three neurons, the second hidden layer comprises K neurons, the output layer is a neuron, and both the D and the K are natural numbers; the input layer is used for receiving the D dimensional vector, and each neuron correspondingly receives one component in the D dimensional vector; the first hidden layer is used for mapping the D dimensional vector received from the input layer to the neurons in the second hidden layer, and each group of the neurons in the first hidden layer corresponds to one neuron in the second hidden layer; the second hidden layer is used for mapping the 3K dimensional vector received from the first hidden layer to the neurons in the output layer; and the output layer performs biased w0 linear weighting to the result of the second hidden layer and then outputs the result. The invention overcomes the extremely small difficulty of being trapped in local and the sensitivity to the noise.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to a main direction neural network system. Background technique [0002] The problem of machine learning research is how to make the machine learn and become intelligent. Artificial neural network is a powerful means to realize machine learning, because the basic starting point of artificial neural network research is to imitate biological neural network. [0003] The artificial neural network is composed of a large number of neurons interconnected. The function of a single neuron can be very simple, but the network formed by the complex connection between them has extremely strong nonlinear mapping, distributed storage, parallel processing and fault tolerance. These are the foundations for realizing intelligence. [0004] The very important advantage of the artificial neural network lies in the realization of nonlinear map approximation, which is a very critical p...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08
Inventor 殷维栋王守觉
Owner INST OF SEMICONDUCTORS - CHINESE ACAD OF SCI
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap