本文選題：物聯(lián)網(wǎng) + 智能家居　； 參考：《吉林大學(xué)》2015年碩士論文

【摘要】：近幾年來，隨著計算機(jī)網(wǎng)絡(luò)技術(shù)的不斷發(fā)展以及與其他技術(shù)的交叉應(yīng)用，物聯(lián)網(wǎng)(Internet of Things)的概念被提出并受到高度重視�，F(xiàn)階段，物聯(lián)網(wǎng)技術(shù)被廣泛應(yīng)用于人們的工業(yè)生產(chǎn)和日常生活中，為人們的生產(chǎn)和生活提供便利和服務(wù)。智能家居(Smart Home)作為物聯(lián)網(wǎng)技術(shù)的研究中的一個典型應(yīng)用，旨在為人們提供簡單、舒適、智能、安全的居住和工作環(huán)境，具有廣闊的應(yīng)用前景和商業(yè)價值，也是物聯(lián)網(wǎng)研究工作的重點之一。智能家居的研究主要包括：嵌入式終端和設(shè)備、無線傳感器網(wǎng)絡(luò)和異構(gòu)網(wǎng)絡(luò)、家居環(huán)境的監(jiān)控與遠(yuǎn)程控制、體感交互技術(shù)(語音識別、手勢識別等)、居家安全系統(tǒng)、輔助(智能)決策系統(tǒng)等幾個方面。目前智能家居中決策系統(tǒng)的設(shè)計過于簡單或者完全依賴于人進(jìn)行�？梢酝ㄟ^機(jī)器學(xué)習(xí)的方法來提高輔助決策系統(tǒng)的智能性，減少人在智能家居控制上精力，實現(xiàn)“以人為本”宗旨，提供簡單、舒適和智能的環(huán)境。本文主要研究BP神經(jīng)網(wǎng)絡(luò)算法在智能家居輔助決策系統(tǒng)中的應(yīng)用。 智能家居環(huán)境中需要考慮人與人之間的差異，不同的人對環(huán)境的要求不盡相同，因此需要考慮不同的人對舒適環(huán)境的要求，在進(jìn)行輔助決策時因人制宜，提高輔助決策的準(zhǔn)確性和智能程度。BP神經(jīng)網(wǎng)絡(luò)具有良好的自適應(yīng)和非線性映射能力，可以通過學(xué)習(xí)掌握智能家居中人的“喜好”，做出最為合適的輔助決策。然而BP神經(jīng)網(wǎng)絡(luò)的準(zhǔn)確度受神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)和訓(xùn)練樣本質(zhì)量的影響較大，因此需要根據(jù)實際環(huán)境，合理的設(shè)計BP神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)，優(yōu)化訓(xùn)練樣本的質(zhì)量，來提升輔助決策系統(tǒng)的準(zhǔn)確性。 不同的人對環(huán)境的要求有差別，導(dǎo)致產(chǎn)生的數(shù)據(jù)相互影響，降低了BP神經(jīng)網(wǎng)絡(luò)訓(xùn)練樣本的質(zhì)量。一種解決方法是通過特殊的方法找出這種差異，對環(huán)境中的人根據(jù)對環(huán)境要求進(jìn)行分組，然后單獨訓(xùn)練出相應(yīng)的決策規(guī)則，輔助決策時對不同的人使用不同的規(guī)則，來保證訓(xùn)練樣本的質(zhì)量，提高輔助決策的準(zhǔn)確性。本文通過對智能家居中人的行為的描述，結(jié)合環(huán)境信息，使用K最近鄰算法，來表現(xiàn)人對環(huán)境要求的差別然后進(jìn)行分組，確保相同小組的人對環(huán)境具有相似的要求，，實現(xiàn)提高決策準(zhǔn)確性和智能性的目的。 綜上所述，本文將神經(jīng)網(wǎng)絡(luò)應(yīng)用于智能家居輔助決策系統(tǒng)中，使用合適的方法來提高樣本質(zhì)量和改進(jìn)神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)，在提升輔助決策系統(tǒng)的準(zhǔn)確性和智能性方面進(jìn)行了嘗試，并通過實驗驗證了本文方法的可行性，和傳統(tǒng)的輔助決策系統(tǒng)比較，具有更好的準(zhǔn)確度和智能性，更符合智能家居“以人為本”的思想。
[Abstract]:In recent years, with the continuous development of computer network technology and the cross-application of other technologies, the concept of Internet of things (Internet of things) has been put forward and attached great importance to. At present, Internet of things technology is widely used in people's industrial production and daily life, providing convenience and service for people's production and life. Smart Home, as a typical application of Internet of things technology, aims to provide people with simple, comfortable, intelligent and safe living and working environment. It has broad application prospect and commercial value. Also is one of the focal points of the Internet of things research. The research of smart home mainly includes: embedded terminal and equipment, wireless sensor network and heterogeneous network, monitoring and remote control of home environment, interactive technology of body sensation (speech recognition, gesture recognition, etc.), home security system, etc. Auxiliary (intelligent) decision system, etc. At present, the design of decision-making system in smart home is too simple or completely dependent on people. The method of machine learning can improve the intelligence of the auxiliary decision system, reduce the energy of the intelligent home control, realize the aim of "people-oriented", and provide a simple, comfortable and intelligent environment. This paper mainly studies the application of BP neural network algorithm in intelligent home aided decision system. In the smart home environment, we need to take into account the differences between people, different people have different requirements for the environment, so we need to consider the requirements of different people for comfortable environment. To improve the accuracy and intelligence of auxiliary decision. BP neural network has a good ability of adaptive and nonlinear mapping. It can make the most appropriate auxiliary decision by learning and mastering the "preferences" of people in the intelligent home. However, the accuracy of BP neural network is greatly affected by the neural network structure and the quality of training samples, so it is necessary to design the structure of BP neural network reasonably and optimize the quality of training samples according to the actual environment. To improve the accuracy of the auxiliary decision system. Different people have different requirements for the environment, which leads to the mutual influence of the generated data and reduces the quality of BP neural network training samples. One solution is to find out this difference in a special way, group the people in the environment according to the requirements of the environment, and then train the corresponding decision rules individually, and then use different rules for different people when assisting in the decision. To ensure the quality of training samples, improve the accuracy of decision-making. In this paper, by describing the behavior of people in smart home, combining the environmental information, using K nearest neighbor algorithm, to express the difference of people's environmental requirements and then to group, to ensure that the same group of people have similar requirements for the environment. The purpose of improving the accuracy and intelligence of decision making is realized. To sum up, this paper applies neural network to intelligent home aided decision-making system, using appropriate methods to improve the quality of samples and improve the neural network structure, in order to improve the accuracy and intelligence of the auxiliary decision-making system. The feasibility of this method is verified by experiments. Compared with the traditional auxiliary decision system, it has better accuracy and intelligence, and is more in line with the idea of "people-oriented" in smart home.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TU855;TP391.44;TN929.5

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