Online Sequential Extreme Learning Machine Method with Self-Learning Capability

A technology with sequence limit and self-learning, applied in the field of intelligent robots, it can solve the problems of poor real-time performance, low intelligence of robot obstacle avoidance and navigation, and low learning rate.

Inactive Publication Date: 2018-07-27
NORTH CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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  • Abstract
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Problems solved by technology

[0006] The patent application No. 201410101272.0 mainly proposes a bionic intelligent control method for traditional robots with low learning rate and poor real-time performance. This method can quickly and effectively improve the intelligence level of robots
The patent with the application number 201410055115.0 mainly aims at the problem that the intelligent level of robot obstacle avoidance navigation in the existing technology is not high. Under certain circumstances, interact with the environment in a "learning-by-doing" trial-and-error manner, establish operational conditioned reflexes, and complete obstacle avoidance navigation

Method used

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  • Online Sequential Extreme Learning Machine Method with Self-Learning Capability
  • Online Sequential Extreme Learning Machine Method with Self-Learning Capability
  • Online Sequential Extreme Learning Machine Method with Self-Learning Capability

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Embodiment Construction

[0074] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0075] The learning frame diagram of the present invention is as image 3 shown, and in accordance with figure 1 The flow shown is for training and learning. figure 2 The intelligent control structure model of the robot is given, which shows how the robot completes collision avoidance through autonomous learning.

[0076] Before an intelligent robot completes a series of tasks, it must first ensure that it can quickly adapt to the environment in real time and complete corresponding tasks. Therefore, the mobile robot recognizes the scene and completes the motion control of avoiding obstacles, which is called the primary task of the robot. In order to verify the validity and convergence of an extreme learning machine model with autonomous learning ability proposed by the present invention, the experiment takes a mobile robot as the research object to ...

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Abstract

The invention relates to an on-line sequence limit learning machine method possessing an autonomous learning capability and belongs to the intelligent robot technology field. Nine portions are included and comprise an external state set, an external motion set, a rewarding signal, a value function, a state transition equation, a limit learning machine network hidden layer output set, an intermediate parameter transfer equation, a limit learning machine output set and a limit learning machine output weight transfer equation. In the on-line sequence limit learning machine method possessing the autonomous learning capability, an on-line sequence limit learning machine is taken as a framework and Q learning is combined and reinforced. The on-line sequence limit learning machine method possessing the autonomous learning capability is provided and a model is used in mobile robot path program research so that a robot realize autonomous learning navigation according to an external environment state and rewarding. An autonomous learning capability of the robot in an unknown environment is increased.

Description

technical field [0001] The invention relates to an online sequence extreme learning machine method with autonomous learning ability, belonging to the technical field of intelligent robots. Background technique [0002] In view of the problems of low initiative and convergence in the existing cognitive development methods and the slow learning speed of BP network, which is easy to fall into local optimum, this patent combines the characteristics of the extreme learning machine network to randomly obtain input weights and thresholds to speed up learning and training Speed, to avoid falling into the local optimal solution, but also improve the active learning performance of the agent. [0003] Exploring the mechanism of cognitive development, constructing the mechanism of cognitive development, and endowing these mechanisms to robots are important topics in the research of artificial intelligence and robotics, cognitive science, neurophysiology and developmental psychology. ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05D1/02
CPCG05D1/0221G05D2201/0217
Inventor 任红格史涛李福进尹瑞张春磊刘伟民霍美杰徐少彬
Owner NORTH CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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