神经网络和模糊系统在故障诊断中的应用

本文提出了一种神经网络和模糊系统相结合的分级式故障诊断方法。神经网络通过对部分测量数据的处理，实现系统的回路级故障诊断，输出各回路故障出现的可信度。模糊系统通过对神经网络得到的初步诊断结果和其他测量值的处理，实现系统的元件级故障诊断，并对最终诊断结果作出解释。该方法融合了神经网络自适应学习能力强和模糊系统知识表达明确的优点，简化了神经网络学习数据获取及模糊推理规则建立的过程。通过对热硝酸冷却系统故障诊断的仿真，证明了该故障诊断方法的有效性。     

智能系统与机器学习

随着计算机科学特别是人工智能的发展,人们对智能系统的研究更加深入,逐步认识到智能系统的核心问题就是它的学习能力。那么,智能系统的一般组成及工作过程怎样?智能系统有哪些性质?要模拟智能系统应该解决哪些问题?尽管当前出现了各种学习系统,如机械学习,指导式学习,发现式学习,示例式学习,类比学习,解释学习等,但这些系统在学习过程中有何作用,处于何种地位?还有哪些学习方式值得我们研究?这就是本文要力求阐明的问题。     

专家系统中背景研究的意义

人工智能中不同领域的研究表明：新一代的智能辅助系统是与背景相关的。虽然人们普遍接受知识应有一个背景部件的观点，但在可用的知识表态方法及随后的知识处理中极少显式表达和利用背景知识。本文着眼于探讨 背景研究在专家系统开发中的意义，以期阐明：背景知识的显式识别、表达与利用有助于专家系统听知识获取、知识表示、推
推理、学习和解释，从而提高专家系统自适应能力和解决问题的智能性。     

解释学习的效用问题

近年来,在机器学习领域,基于解释的学习引起了广泛的兴趣。解释学习是一种演绎学习方法,根据领域理论对训练实例进行解释,经过推广后获得新的知识。从可操作性的角度看,解释学习并没有学到真正新的知识,只是一种知识转换,它把原先不可操作的概念描述转换为可操作的目标概念描述,而使系统的性能得以提高。但原来的可操作性定义并没有考虑到解释学习的效用(utility)问题,人们发现这样的解释学习并不总能提高系统的性能,而是在大多数情况下会使系统的行为比未经学习时更差,这使得效用问题与不完善理论问题一样成为解     

基于解释的算法构架的学习

本文提出了将解释学习方法用于学习算法构架的思想，以提高软件自动化系统从功能规格说明转换到设计规格说明的能力．文中给出了算法构架的表示，操作性的定义及其处理方法．系统从用户给出的一个问题的解中学习算法构架，用于解决一类问题，系统的学习效果表现为通过学习能够解决原来不能解的问题．     

基于BP网络的模糊Petri网的学习能力

模糊Petri网(Fuzzy Petri Nets，FPN)是基于模糊产生式规则的知识库系统的良好建模工具，但自学习能力差是模糊系统本身的一个缺点．该文提出了适合模糊Petri网模型自学习的模糊推理算法和学习算法．在模糊推理算法中，通过对没有回路的FPN模型结构进行层次式划分以及建立变迁点燃和模糊推理的近似连续函数，从而把神经网络中的BP网络算法自然地引入到FPN模型中．在FPN模型上，用误差反传算法计算一阶梯度的方法对模糊产生式规则中的参数进行学习和训练．经过学习和训练的FPN具有很强的泛化能力和自适应功能．FPN模型经过训练得到的参数是有特定含义的，可以通过对这些参数的合法性分析，使得模糊产生式规则系统更加有效，也对知识库系统的建立、更新和维护有着重要的意义．     

为不完全领域理论补充知识的学习方法CE

本文给出了一个补充解释的学习ＣＥ，是解释学习和归纳学习两种方法的结合，试图解决完全的领域理论补充知识的问题。     

ECSN神经网络系统的解释部件

前言解释在传统人工智能(AI)系统中是一个关键的功能部件,但它在非结构化的神经网络系统里却难以实现,因为NN没有显式,陈述(说明式)知识结构来允许解释的表示和产生.     

示例式学习及多功能学习系统AE5

近几年来,机器学习已经成为人工智能与认识心理学研究的焦点,在各种学习方法中,示例式学习被看作是基础和自动建立基于知识的系统的关键,本文先概述示例式学习的一般概念和理论,然后重点介绍一个有效的多功能学习系统AE5,AE5是已存在的学习系统AE1的一个扩充,它具有构造性学习、渐近式学习与测试等多种功能,AE5还可以用做一个自动知识获取系统。     

驾驭式解释系统的实现

提出了一种全新的驾驭式解释机制。驾驭式解释系统是在推理过程中直接监视其执行过程,不仅可以让用户观察到推理的逻辑思路和中间结果,而且用户还可以直接个性某些信息,如隶属函数,甚至一些中间结果,然后再进一步进行推理。所以推理过程对用户来说是透明的,用户可以干预推理过程。在飞机总体外形设计模糊专家系统（ＡＣＤＳ－ＦＥＳ）中,解释系统就是利用驾双式解释机制实现的。     

Neurocontrollers trained with rules extracted by a genetic assistedreinforcement learning system

This paper proposes a novel system for rule extraction of temporal control problems and presents a new way of designing neurocontrollers. The system employs a hybrid genetic search and reinforcement learning strategy for extracting the rules. The learning strategy requires no supervision and no reference model. The extracted rules are weighted micro rules that operate on small neighborhoods of the admissable control space. A further refinement of the extracted rules is achieved by applying additional genetic search and reinforcement to reduce the number of extracted micro rules. This process results in a smaller set of macro rules which can be used to train a feedforward multilayer perceptron neurocontroller. The micro rules or the macro rules may also be utilized directly in a table look-up controller. As an example of the macro rules-based neurocontroller, we chose four benchmarks. In the first application we verify the capability of our system to learn optimal linear control strategies. The other three applications involve engine idle speed control, bioreactor control, and stabilizing two poles on a moving cart. These problems are highly nonlinear, unstable, and may include noise and delays in the plant dynamics. In terms of retrievals; the neurocontrollers generally outperform the controllers using a table look-up method. Both controllers, though, show robustness against noise disturbances and plant parameter variations.     

Explanation-based learning:A problem solving perspective

This article outlines explanation-based learning (EBL) and its role in improving problem solving performance through experience. Unlike inductive systems, which learn by abstracting common properties from multiple examples, EBL systems explain why a particular example is an instance of a concept. The explanations are then converted into operational recognition rules. In essence, the EBL approach is analytical and knowledge-intensive, whereas inductive methods are empirical and knowledge-poor. This article focuses on extensions of the basic EBL method and their integration with the problem solving system. 's EBL method is specifically designed to acquire search control rules that are effective in reducing total search time for complex task domains. Domain-specific search control rules are learned from successful problem solving decisions, costly failures, and unforeseen goal interactions. The ability to specify multiple learning strategies in a declarative manner enables EBL to serve as a general technique for performance improvement. 's EBL method is analyzed, illustrated with several examples and performance results, and compared with other methods for integrating EBL and problem solving.     

EXPLANATION-BASED NATURAL LANGUAGE ACQUISITION USING UNIVERSAL LINGUISTIC PRINCIPLES AS INNATE DOMAIN THEORY

In this paper, we propose a new natural language acquisition model (called EBNLA) based on explanation-based language ( EBL). To apply EBL to the natural language acquisition domain, suitable universal linguistic principles are incorporated as domain theory. The domain theory consists of two parts: static and dynamic. The static part, which is assumed to he invariant and innate to the model, includes theta theory in government-binding theory and universal fea ture instantiation principles in generalized phrase structure grammar. The dynamic part con tains context-free grammar rules as well as syntactic and thematic features of lexicons. In parsing ( problem solving), both parts work together to parse input sentences. As parsing fails, learning is triggered to enrich and generalize the dynamic part by obeying the principles in the static part. By introducing EBL and the universal linguistic principles, portability of the model and leamabitity of knowledge in the real-world natural language acquisition domain can be improved.     

An introduction to learning

At a symbolic level cognition can be modelled as a production system where meaning units are represented as condition-action rules. Anderson (1982, 1987) provides a good example of how learning can occur with this type of knowledge representation. At a subsymbolic level cognition can be modelled with a connectionist network where meaning units are represented as patterns of parallel distributed activity. The work of the McClelland and Rumelhart (1986) group is a prototype of this approach. We elaborate on these two approaches to learning and contrast the symbolic search space paradigm with the connectionist paradigm.     

适应噪声环境的解释学习算法

在现实世界里，ＡＩ系统难免受到噪声的影响．系统有效工作与否取决于它对噪声的敏感性如何．解释学习ＥＢＬ（ｅｘｐｌａｎａｔｉｏｎ－ｂａｓｅｄｌｅａｒｎｉｎｇ）也不例外．本文探讨了在例子受到噪声影响的情况下，解释学习的处理问题，提出了一个算法ＮＲ－ＥＢＬ（ｎｏｉｓｅ－ｒｅｓｉｓｔａｎｔＥＢＬ）．与现有的解释学习方法不同，ＮＲ－ＥＢＬ在训练例子含有噪声时仍然可以学习，以掌握实际的问题分布；和类似的工作不同，ＮＲ－ＥＢＬ指出了正确识别概念对于噪声规律的依赖性，试图从训练例子集合发现和掌握噪声的规律．可以相信，在识别概念时，借助于对噪声规律的认识，ＮＲ－ＥＢＬ可比ＥＢＬ和类似工作有更高的识别率．ＮＲ－ＥＢＬ是解释学习和统计模式识别思想的结合．它把现有的解释学习模型推广到例子含有噪声的情形，原来的ＥＢＬ算法只是它的特例．     

Genetic reinforcement learning through symbiotic evolution forfuzzy controller design

An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is proposed in this paper. The genetic algorithm (GA) adopted in this paper is based upon symbiotic evolution which, when applied to fuzzy controller design, complements the local mapping property of a fuzzy rule. Using this Symbiotic-Evolution-based Fuzzy Controller (SEFC) design method, the number of control trials, as well as consumed CPU time, are considerably reduced when compared to traditional GA-based fuzzy controller design methods and other types of genetic reinforcement learning schemes. Moreover, unlike traditional fuzzy controllers, which partition the input space into a grid, SEFC partitions the input space in a flexible way, thus creating fewer fuzzy rules. In SEFC, different types of fuzzy rules whose consequent parts are singletons, fuzzy sets, or linear equations (TSK-type fuzzy rules) are allowed. Further, the free parameters (e.g., centers and widths of membership functions) and fuzzy rules are all tuned automatically. For the TSK-type fuzzy rule especially, which put the proposed learning algorithm in use, only the significant input variables are selected to participate in the consequent of a rule. The proposed SEFC design method has been applied to different simulated control problems, including the cart-pole balancing system, a magnetic levitation system, and a water bath temperature control system. The proposed SEFC has been verified to be efficient and superior from these control problems, and from comparisons with some traditional GA-based fuzzy systems.     

Explanation-Based Learning for Diagnosis

We present explanation-based learning (EBL) methods aimed at improving the performance of diagnosis systems integrating associational and model-based components. We consider multiple-fault model-based diagnosis (MBD) systems and describe two learning architectures. One, EBLIA, is a method for learning in advance. The other, EBL(p), is a method for learning while doing. EBLIA precompiles models into associations and relies only on the associations during diagnosis. EBL(p) performs compilation during diagnosis whenever reliance on previously learned associational rules results in unsatisfactory performance—as defined by a given performance threshold p. We present results of empirical studies comparing MBD without learning versus EBLIA and EBL(p). The main conclusions are as follows. EBLIA is superior when it is feasible, but it is not feasible for large devices. EBL(p) can speed-up MBD and scale-up to larger devices in situations where perfect accuracy is not required.     

Adaptive Default Hierarchy Formation

Autonomous systems are likely to be required to face situations that cannot be foreseen by their designers. The potential for perpetually novel situations places a premium on mechanisms that allow for automatic adaptation in a general setting. The term reinforcement learning problems (Mendel and McLaren, 1970) generally describes problems where a control system must adapt based on performance-only feedback. This paper considers the learning classifier system (LCS) as an approach to reinforcement learning problems. An LCS is a type of adaptive expert system that uses a knowledge base of production rules in a low-level syntax that can be manipulated by a genetic algorithm (GA) (Holland. 1975; Goldberg, 1989) Genetic algorithms comprise a class of computerized search procedures that are based on the mechanics of natural genetics (Goldberg, 1989; Holland. 1975). An important feature of the LCS paradigm is the possible adaptive formation of default hierarchies (layered sets of default and exception rules) )Holland et al., 1986). This paper examines the problem of default hierarchy formation under the conventional bid-competition method of LCS conflict resolution, and suggests the necessity auction and a separate priority factor as modifications to this method. Simulations show the utility of this method. Final discussion presents conclusions and suggests avenues for further research     

A heuristic method for learning Bayesian networks using discrete particle swarm optimization

Bayesian networks are a powerful approach for representing and reasoning under conditions of uncertainty. Many researchers aim to find good algorithms for learning Bayesian networks from data. And the heuristic search algorithm is one of the most effective algorithms. Because the number of possible structures grows exponentially with the number of variables, learning the model structure from data by considering all possible structures exhaustively is infeasible. PSO (particle swarm optimization), a powerful optimal heuristic search algorithm, has been applied in various fields. Unfortunately, the classical PSO algorithm only operates in continuous and real-valued space, and the problem of Bayesian networks learning is in discrete space. In this paper, two modifications of updating rules for velocity and position are introduced and a Bayesian networks learning based on binary PSO is proposed. Experimental results show that it is more efficient because only fewer generations are needed to obtain optimal Bayesian networks structures. In the comparison, this method outperforms other heuristic methods such as GA (genetic algorithm) and classical binary PSO.