Room temperature control algorithm based on fuzzy neural network

A technology of fuzzy neural network and control algorithm, which is applied in the direction of temperature control using electric mode, auxiliary controller with auxiliary heating device, adaptive control, etc., can solve the lack of adaptive ability, difficult to learn control rules, fuzzy Lost information, etc.
CN104133372BInactive Publication Date: 2016-09-28HOHAI UNIV CHANGZHOU
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Patents(China)
Current Assignee / Owner
HOHAI UNIV CHANGZHOU
Publication Date
2016-09-28
Estimated Expiration
Not applicable Β· inactive patent

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Abstract

The invention discloses a room temperature control algorithm based on a fuzzy neural network. A fuzzy neural network controller with dual input and single output is designed. By detecting the room temperature tracking output and temperature setting value in real time, combined with an online learning mechanism, the controller can be adjusted in real time. Adjustable parameters to adapt to room temperature changes and track temperature setpoints. The invention integrates the learning and computing functions of the neural network into the fuzzy system, and embeds the human-like IF-Then rules of the fuzzy system into the neural network. Self-learning ability.
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Description

technical field

[0001] The invention relates to a room temperature control algorithm based on a fuzzy neural network, which belongs to the field of automatic control of air conditioners. Background technique

[0002] The air-conditioning room system is a complex control object with nonlinear, large time-delay, strong coupling and time-varying characteristics, and is also subject to many uncertainties, such as the flow of people in the room, the heating of various electrical equipment, and the opening and closing of doors and windows Wait.

[0003] At present, PID control is widely used because of its simple principle, strong applicability and strong robustness. However, PID performs poorly in controlling complex processes with nonlinear, time-varying, coupled, and uncertain parameters and mechanisms.

[0004] Intelligent control has the ability of self-learning and self-adaptation, and has good control effects on linear and nonlinear systems, and can well solve the control...

Claims

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