Probabilistic Neural Network based Managing Algorithm for Building Automation System

S. Shakya
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Abstract

A building automation system is a centralized intelligent system, which controls the operation of energy, security, water, and safety by the help of hardware and software modules. The general software modules employed for automation process have an algorithm with pre-determined decisions. However, such pre-determined decision algorithms won’t work in a proper manner at all situations like a human brain. Therefore a human biological inspired algorithms are developed in recent days and termed as neural network algorithms. The Probabilistic Neural Network (PNN) is a kind of artificial neural network algorithm which has the ability to take decisions same as like of human brains in an efficient way. Hence a building automation system is proposed in the work based on PNN for verifying the effectiveness of neural network algorithms over the traditional pre-determined decision making algorithms. The experimental work is further extended to verify the performances of the basic neural network algorithm called Convolution Neural Network (CNN).
基于概率神经网络的楼宇自动化系统管理算法
楼宇自动化系统是一个集中的智能系统,通过硬件和软件模块对能源、安防、水、安全等各个环节的运行进行控制。用于自动化过程的通用软件模块都具有预先确定决策的算法。然而,这种预先确定的决策算法并不能像人类大脑那样在所有情况下都以适当的方式工作。因此,近年来一种受人类生物学启发的算法被开发出来,并被称为神经网络算法。概率神经网络(Probabilistic Neural Network, PNN)是一种人工神经网络算法,它具有和人脑一样的高效决策能力。因此,本文提出了一种基于PNN的楼宇自动化系统,以验证神经网络算法相对于传统的预定决策算法的有效性。实验工作进一步扩展,以验证卷积神经网络(CNN)的基本神经网络算法的性能。
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