人工智能(复习版).doc

杰的小灵儿整理，版权问题，请勿删除，谢谢！人工智能复习指南一、术语解释1、人工智能artificial intelligence 2、知识knowledge 3、专家系统expert system4、产生式规则production rules 5、前向链接forward chaining 6、后项链接backward chaining 7、冲突conflict 8、推理引擎inference engine 9、排中律law of the excluded middle 10、贝叶斯推理Bayesian reasoning11、确定因子certainty factors 12、模糊集fuzzy set 13、模糊逻辑fuzzy logic 14、语言变量linguistic variable 15、模糊限制语hedge 16、模糊规则fuzzy rule 17、模糊推理fuzzy inference 18、模糊关联记忆fuzzyassociative memory 19、质心技术centroid technique 20、隶属函数membership function 21、单态模式singleton 22、人工神经网络artificial neural networks 23、神经元neuron 24、权重weight 25、符号激活函数sign activation function 26、感知器perceptron 27、线性分割函数linear separable function 28、超平面hyper plane 29、向后传送back-propagation 30、硬限幅函数hard-limit function 31、阶跃函数step function 32、多层神经网络multilayer neural networks 33、隐含层hidden layer 34、自反馈self-feedback 35、循环神经网络recurrent neural network 36、双向相关记忆bidirectional associative memory 37、监督学习supervised learning 38、无监督学习unsupervised learning 39、竞争学习competitive learning 40、自组织神经网络self-organizing neural networks 41、存储模式对store pattern pairs 42、基本记忆fundamental memory 43、自组织特征映射self-organizing feature map 44、进化计算evolutionary computation 45、遗传算法genetic algorithms 46、染色体chromosome 47、基因gene 48、进化策略evolution strategy 49、遗传编程genetic programming 50、突变操作mutation operator 51、交叉操作crossover operator 52、性能图performance graph 53、模式定理schema theorem 54、秩rank 55、链表处理语言linked list processing language 56、知识工程knowledge engineering 57、数据挖掘data mining 58、原型prototype 59、测试用例test case 60、系统维护system maintenance 二、简述第1章1、图灵测试内容。（网上总结的）A man without the knowledge of the condition is through a special way, with a machine to answer questions. If in a quite long time, he could not tell the difference between the object of communicate with him is people or machines, then this machine can be considered to thinking. This is the famous Turing test.2、智能的定义。（P2）Intelligence is their ability to understand and learn things. Intelligence is the ability to think and understand instead of doing things by instinct or automatically.3、人工智能的定义。（P18 加点第二段）Artificial intelligence is a science that make machines do things that would require intelligence if done by humans. 第2章4、什么是产生式规则及其组成？（P26、P26 2.2）（1）These statements represented in the IF-THEN form are called production rules.（2）Any rule consists of two parts: the IF part, called the antecedent (premise or condition) and the THEN part called the consequent (conclusion or action).专家系统的基本结构5、描述前向链接和后向链接推理技术。（1）Forward chaining is the data-driven reasoning. The reasoning starts from the known data and proceeds forward with that data. Each time only the topmost rule is executed. When fired, the rule adds a new fact in the database. Any rule can be executed only once. The match-fire cycle stops when no further rules can be fired.（P37）（P38）（2）Backward chaining is the goal-driven reasoning. In backward chaining, an expert system has the goal (a hypothetical solution) and the inference engine attempts to find the evidence to prove it. First, the knowledge base is searched to find rules that might have the desired solution. Such rules must have the goal in their THEN (action) parts. If such a rule is found and its IF (condition) part matches data in the database, then the rule is fired and the goal is proved. （P38）（P39）第三章6、贝叶斯推理、确定因子技术的定义区别及适用情况。（1）（P62）（2）Certainty factor is a number to measure the experts belief. The maximum value of the certainty factor is, say, +1.0 (definitely true) and the minimum -1.0 (definitely false). A position value represented a degree of belief and a negative a degree of disbelief.（P74 3.6中间） （3）Certainty factors are used if the probabilities are not known or cannot be easily obtains.（P84最后一段）第4章7、模糊集、模糊推理、模糊逻辑、隶属函数的定义。（1）A fuzzy set is a set with fuzzy boundaries.（P91下面）（2）Fuzzy inference can be defined as a process of mapping from a given input to an output, using the theory of fuzzy sets.（P106 4.6）（3）Fuzzy logic was introduced by Jan Lukasiewicz in the 1920s. Fuzzy logic is a set of mathematical principles for knowledge representation based on degrees of membership rather than on the crisp membership of classical binary logic. Unlike two-valued Boolean logic, fuzzy logic is multi-valued.（P125 加点第二、三段）（4）Membership function uA(x) equals the degree to which x is an element of set A. （P92英文的第三段中间）8、画图表示模糊集。一个模糊集的表示：tall man=(0/180,0.5/185,1/190)画图：（P93）9、模糊集的操作(补，交，并，包含)。（P98-P100）一、Complement（补）(非A的模糊操作)tall men not tall men二、Containment（包含）tall men very tall men 三、Intersection（交）tall men average men 四、Union（并）tall men average men Operations of fuzzy sets（模糊集的操作）（P101）第6章（重点）10、人工神经网络的定义及工作方式。（1）An artificial neural network consists of a number of very simple and highly interconnected processors, also called neurons, which are analogous to the biological neurons in the brain. （P167 第二段开头）（2）The neurons are connected by links, and each link has a numerical weight associated with it. Weights are the basic means of long-term memory in ANNs. They express the strength, or in other words importance, of each neuron input. A neural network learns through repeated adjustments of these weights.（P167 第三段）典型的人工神经网络的结构11、后向传送神经网络的定义。（P176 最后两段）a back-propagation one is determined by the connections between neurons, the activation function used by the neurons, and the learning algorithm that specifies the procedure for adjusting weights. A back-propagation network is a multilayer network that has three or four layers. The layers are fully connected.12、感知器的定义、结构图和训练算法（1）Frank Rosenblatt suggested the simplest form of a neural network, which he called a perceptron. The operation of the perceptron is based on the McCulloch and Pitts neuron model. It consists of a single neuron with adjustable synaptic weights and a hard limiter. The perceptron learns its task by making small adjustments in the weights to reduce the difference between the actual and desired outputs. The initial weights are randomly assigned and then updated to obtain the output consistent with the training examples.（P213 加点第二段）（2）结构图、计算（P168）、（公式：P169、P171）神经元示意图 （误差）（3）Perceptrons training algorithm（训练算法）（P172）Step 1: Initialisation（初始化） Set initial weights w1, w2, wn and threshold to random numbers in the range -0.5, 0.5.Step 2: Activation （激活） Activate the perceptron by applying inputs x1(p), x2(p), xn(p) and desired output Yd (p). Calculate the actual output at iteration p = 1where n is the number of the perceptron inputs, and step is a step activation function.Step 3: Weight training（权重训练） Update the weights of the perceptron。where Dwi(p) is the weight correction at iteration p.The weight correction is computed by the delta rule:Step 4: Iteration （迭代） Increase iteration p by one