Study On Learning And Control Strategy For Intelligent Inhabited Environments

With the improving of living level, the people have higher demands for their inhabited environments. How to maximally meet their comfortable need and reduce energy consumption and other potential waste are the goals of HE (intelligent inhabited environments). HE are enclosed living spaces equipped with embedded intelligent equipments that connect with residential equipments through a range of internal network, which can provide a variety of services by connecting with external world and actively respond to the environments according to the needs of the users, while maintaining these equipments coordinating with residences. HE can be characterized by its ubiquity, transparency and intelligence. As the information edge structure of social system, the HE closely relate to the people's daily lives and they are inseparable with the functions and features as the information society units. Therefore, not only the perfect and reasonable open architecture should be established under the guidance of macro theory, but also the user's specific needs should be considered from the micro point, which make the system more personal and intelligent. With the development of computer, microelectronic, communication and intelligent control, the theory and application technology for HE were researched widely in recent 10 years. But the current researches lacked of perfect theoretical system in macro and did not consider historic time, the self-learning and adaptive capacity of system in micro. The study projects of the major companies focused on how to achieve communication between homes and equipments. Most of the home automation technology solutions usually only adopted simple automation, and rarely used artificial intelligence, and little emphasized the learning and adaptive of user behaviors.This paper is devoted to further study the theoretical system of HE and built an open Multi-Agent System (MAS) architecture. According to the characteristics of HE, the reasoning, learning and control strategies are studied to improve the decision-making of system, which can meet the needs of users'comfort and maximize the energy efficiency. The main research work and innovative fruits are as follows.1. A MAS architecture developed for HE based on ZigBee wireless sensor network is presented. The management agent in the management layer corresponds to the coordinator of ZigBee network. The function agents in the function layer correspond to the routers of ZigBee network. The bus agents in the bus layer correspond to the end-devices of ZigBee network. Moreover, the structure models of bus agent, function agent, and management-agent are built to provide the infrastructure for the cooperation, conflict elimination, and organizational alliance of the agents.2. The user's activities recognition is one of the key technologies to realize ubiquitous, transparent and intelligent inhabited environments. A novel One-Pass neural network system is presented which is able to recognize different high level activities (such as "sleeping", "learning", "listening music", et al) based on simple sensors(such as "move sensor", "pressure sensor", et al) for HE. The neural networks architecture includes three layers which are input layer, middle layer and output layer. The input layer is sensor states layer. The output layer is user behaviors layer. The middle layer is hidden layer and its dimension is same as the output layer. The neural network system adding temporal informations is able to recognize abnormal behaviors. Due to the fact that the One-Pass learning method of weight ratios has the characters of simplicity, no iteration, and lower memory, the embedded computers can be trained in an online mode. Experiment results show that this method is transparent, simple and effective.3. The intelligent fuzzy agent based on user preference learning and the input-output dynamic associated algorithm based on Hebb neural network are proposed, respectively. On the basis, a novel dynamic associated intelligent fuzzy agent is build that associates the dynamic associated algorithm with the intelligent fuzzy agent. It can realize the learning of user preferences, and then actively control the devices of inhabited environment. The different user's preferences and needs are different In HE, and the same user's preferences will change over time, which desire that the intelligent agents should have evolvement function. The intelligent fuzzy agent proposed includes five phases:?Capturing input-output data pairs.?Membership function learning.?Extracting fuzzy rules.?Agent control.?Adaptive learning algorithm online. Initially the system's fuzzy rules are extracted from the collected information of sensors and actuators in HE, after that the system rapidly optimizes the fuzzy rules when the user's preferences change. Moreover, the interconnected embedded agents by network have the capabilities of intelligent reasoning, planning and learning in HE. However, the multitude of interconnected embedded agents can result in major load in network communication and calculation, which decreases the system's execution rate. A novel dynamic structure association algorithm for embedded agents in HE is proposed based on Hebb neural network. The association weight values between sensor agents and device agents will be calculated and updated if an event occurs. The embedded agents can be dynamicly divided into multiple groups according to association weight matrix, and then the fuzzy rules base can be divided into multiple sub-fuzzy rules bases. The methods proposed can reduce the number of fuzzy rules, improve the learning rates of agents, and decrease the network communication among embedded agents.4. The inhabited environment is a kind of very complex and control difficultly high-dimensional nonlinear system. It is hard to build the system's dynamical mathematical model with the conventional methods. According to the characteristics of HE, fully using the accumulated historical data of sensor status and equipment operations during the system's long-term running, an improved hyperball CMAC neural network algorithm based on clustering is proposed for HE nonlinear dynamic modeling of predicted control. A fuzzy clustering algorithm is adopted to determine the node number and node locations by clustering the input data. A fuzzy inference optimization algorithm is proposed to determine the initial weight values of neural network based on input-output data. Compared with the hyperball CMAC, the improved algorithm can effectively reduce the neural network nodes and improve the learning accuracy.5. The static thermal environment is unfavorable to the human's health as it can reduce the ability of human's heat adaptation. The dynamic thermal environment is favorable to the human's health as it is similar to the natural environment. A dynamical thermal comfortable control system is presented for the inhabited environment based on the learning of human's thermal comfort zone. The fuzzy learning algorithm of personal thermal comfort zone is proposed based on predicted mean vote (PMV) index, which can modify the personal thermal comfort zone on line with the learning of human's thermal preference to meet the needs of different humans. The dynamical thermal comfort control strategy is proposed with computational experiments, which make the thermal comfort zone and energy saving zone change periodically. The experiment results demonstrate that this method can meet the human's thermal comfort need and reduce the energy consumption, whilst it is favorable to the human's health.In sum, the learning and control strategies for HE are studied deeply in this paper. The research results can be used to provide technology support of system platform building for HE. Moreover, the research results can be used to provide the theoretical basis for user's behavior identification, user's adaptive, long-term learning mechanism, system nonlinear modeling, dynamic thermal comfort control, et al.