Integrating Neural Network and Expert Reasoning: An Example
Leonard Dickens1991, Springer eBooks
Sign up for access to the world's latest research
checkGet notified about relevant papers
checkSave papers to use in your research
checkJoin the discussion with peers
checkTrack your impact
Abstract
In this paper we describe a shell which has been developed to allow an integration of neural network and expert systems technology. The system, SCRuFFy, is based on an analysis of the different abilities and time courses of NN and Al systems. Critical to the processing of this system is a temporal pattern matcher which is used to mediate between the two subsystems, providing a "signal to symbol" mapping. This mapping allows the expert system to reason about the time course of signals which are classified by a connectionist netwo) which is trained via classical back-propagation of error during a separate training phase. An example of the simulated control of the temperature of an underwater welding robot is presented to demonstrate these capabilities.
Related papers
A neural network approach for implementing expert systems
Neural Networks, 1988
Applications usin~ expert systems are gaining in popularity. These systems apply expert knowledge about a particular field and logically progress through & given problem to arrive at an appropriate solution. Problems such as nuclear power plant monitoring, robot ma~Lipu~on, medical diagnosis are just a few examples of applications using expert systems. Present day expert systems are written on higher-level interpretive languages, st{ch as LISP and PRO~, running on fairly expeusi~;e computers. Even on these machines, the speed-performances of the expert systems are less than satisfactory as the number of rules increases. Hence these system are unsuitable for time-critical classification applications. Recently there is tremendous interest in artifical neural networks, which allows the parallel execution of competing hypothesis using massively parallel networks composed of many computational elements connected by links with variable weights. Implementation of expert systems using this ne.twork structure can improve the speed-performance of the system, and reduce complexity and time requirement for building the system. A methodology for building an expert system is developed. The methodology is based on the current concept of building expert systems, such as knowledge acquisition and representation, data collection, user interfacing. Additions] concepts of system learning(training), acceptability are also included. For demonstration purposes, a neural network learnin.g a]~orithm has been applied to develop a diagnostic expert system for patients with chest pain. The system co~ectly classifies four categories of ch~t pain: myo.c~, dial infarction, ischemic pain, non-ischemlc pain and non-cardiac pain. Simulatlon results will be presented in the paper and the conference to demonstratethe viability of the approach. The superiority of the neural networks approach will be proved by comparison of speed-performance o]~ neural networks approach vs. statistical approach. The system performance was comparable to the physicians.
Integrating Expert Systems and Neural Computing for Decision Support
1994
systems are used today either as stand-alone or in conjunction with other computer-based information systems (CBIS) in thousands of organizations worldwide to provide decision support for solving problems that traditional information systems were unable to solve. Neural computing is an emerging promising technology with few successful applications. By integrating the two technologies one can achieve some improvements in the implementation of each as well as increase the scope of application. We provide a comprehensive framework and examples of such an integration.
Towards integrating rule-based expert systems and neural networks
Decision Support Systems, 1996
This research explores a new approach to integrate neural networks and expert systems. The integrated system combines the strength of rule-based semantic structure and the learning capability of connectionist architecture. In addition, the approach allows users to define logical operators that behave much similar to that of human expert decision making process. Neural Logic Network (NEULONET) is used as the underlying building unit. A rule-based shell like environment is developed. The shell is used to built a prototype expert decision support system for future bonds trading. The system also provides a way to behave like different experts responding to different users and giving advice according: to different environmental situations.
Hybrid Expert SystemsAn Approach to Combining Neural Computation and Rule-Based Reasoning
Expert Systems, 2002
Neuro-symbolic reasoning system for modelling complex behaviours
A neuro-symbolic reasoning strategy for modelling a complex system is presented in which the aim is to forecast, in real time, the physical parameter values of a dynamic environment: the ocean. In situations in which the rules that determine a system are unknown the prediction of the parameter values that determine the characteristic behaviour of the system can be a problematic task. In such a situation it has been found that a case-based reasoning system, in combination with an artifical neural network, can provide a more effective means of performing such predictions than other connectionist or symbolic techniques. The case-based reasoning system incorporates a radial basis function artificial neural network for the case adaptation. The results obtained from experiments, in which the system operated in real time in the oceanographic environment, are presented.
Using SIGNAL for developing neural control systems
OCEANS Conference, 1994
We present in this paper a new environment for developing neural networks integrated in process control loops. This environment allows the user to easily control the learning phase and assures the user that the obtained system is both logically and temporally correct. Those two aspects have to be proven in order to obtain a viable real-time system. The conventional languages
A STUDY ON ARTIFICIAL NEURAL NETWORKS
A STUDY, 2018
First step towards AI is taken by Warren McCulloch a neurophysist and a mathematician Walter Pitts. They modelled a simple neural network with electrical circuits and got the results very accurate and derived a remarkable ability of neurons to perceive information from complicated and imprecise data. During the present study it was observed that trained neural network expert in analyzing the information has been provided with other advantages as Adaptive learning, Real Time operation, self-organization and Fault tolerance as well. Apart from convectional computing, neural networking use different processing units (Neurons) in parallel with each other. These need not to be programmed. They function just like human brain. We need to give it examples to solve different problems and these examples must be selected carefully so that it would not be waste of time.we use combination of neural networking and computational programming to achieve maximal efficiency right now but neural networking will eventually take over in future. We introduced artificial neural networking in which electronic models where used as neural structure of brain. Computers can store data as ledgers etc. but have difficulty in recognizing patterns but brain stores information as patterns. Further as artificial neural networking was introduced which has artificial neurons who act as real neurons and do functions as they do. They are used for speech, hearing, reorganization, storing information as patterns and many other functions which a human brain can do. These neural networks were combined and dynamically self-combined which is not true for any artificial networking. These neurons work as groups and sub divide the problem to resolve it. These are grouped in layers and it is art of engineering to make them solve real world problems. The most important thing is the connections between the neurons, it is glue to system as it is excitation inhibition process as the input remains constant one neuron excites while other inhibits as in subtraction addition process. Basically, all ANN have same network that is input, feedback or hidden and output.
Artificial Neural Network Applications to Control
The use of Artificial Neural Networks (ANNs) for the control of robots has recently become an active research topic in control engineering. This paper is presented a review of current research in control of robotic systems based on ANN techniques. The main concepts of ANNs are presented and the mathematical background of supervised learning in multilayer networks is outlined in first section. A brief summary on adaptive control of dynamic systems is given second section. The application of ANNs to control systems is discussed in the last section. uygulanması detaylıca sunulmuştur. ANNs are made up of simple, highly interconnected processing units called neurons each of which performs two functions: aggregation of its inputs from other neurons or the external environment and generation of an output from the aggregated inputs. The output from a neuron is fed to other neurons to which it is connected via weighted links. Through this simple structure, ANNs have been shown to be able to ...
Modeling human operators using neural networks
ISA Transactions, 1992
Ojgen in the procus indusm'u, INTRODUCTION often does not quite know what they are doing while performing human operators rather than The ultimate goal in modeling their jobs; that is, many of the mathematically based advanced a human plant operator's perfor-tasks are performed on an unconalgorithms are used to achieve mance is to relieve the human of scious level and it may be difficult product control. Typically, if tasks that can be performed better to identify or explain them on a there are ~Sree shifts of operators through automation. Another conscious level. Also, if operators per clay, one shift achieves supe. major goal is to apply more con-feels threatened by a technology riot control. If the expertise of sistent control to the plant. Often, that they feel might replace them the best operator can be capoperators vary greatly from one in their jobs, they may be unwill. shift to another. This variance can. cured easily and economically greatly affect thI quality of the ,ng to help generate the required and made available to the other product and reduce the overall ef-knowledge base. The current research proposes operators, significant economic ficiency of the plant, the use of a neural network as the benefits would accrue. This Plant operators are typically en-paFer discusses a methodology gaged in a supervisory control kernel of an operator model. Neuthat uses artificial neural nettask. Such a task consists of moni. ral networks have the ability to learn mappings o£ a function sireworks for capturing the knowlcoring several subtasks and engagedge of process operators. For ing in fault detection and failure ply by being trained on examples many operator tasks, only compensation. Models for human of the function. Simplistically, behavior in a supervisory control this property can be thought of as readily available information task already exist. These models the ability to abstract. obtained from a t~rocess control often contain a knowledge-based A knowledge base is also recomputer is required. Once a (heuristic-based) kernel that relies quired for a neural network converged network is available, on rules the human operator must model. Nevertheless, the required a stripping technique can he erasupply, data should be relatively simple to ployed to slm~llfy the net and to Obtaining the required rules obtain. To train the neural net. gain knowledge about what a for the knowledge base can be work, all that is needed are the good operator is doing compared quite difficult since operators a:.tual responses of an operator to to a poor ont. It is felt that the #ro#os~a approach is superior to Neural networks have the ability to learn mappings traditional expert system tech. of a fimction simply by being trained on examples of niques employing knowledge u. the f, nction.
Related topics
Related papers
Integrating neural networks and knowledge-based systems for robotic control
Proceedings, 1989 International Conference on Robotics and Automation, 1989
Artificial neural networks and knowledge-based systems offer very different capabilities concerning control system design, implementation, and performance. This paper addresses the issue of integrating both computational paradigms for the purpose of robotic manipulation. The control task chosen to demonstrate the integration technique involves teaching a two-link manipulator how to make a tennis-like swing. A three-level task hierarchy is defined consisting of low-level reflexes, reflex modulators, and an execution monitor. The rule-based execution monitor first determines how to make a successful swing using rules alone. It then teaches a neural network how to accomplish the task by having it observe rule-based task execution. Following initial training, the execution monitor continuously evaluates neural network performance and re-engages swing-maneuver rules whenever changes in the manipulator or its operating environment necessitate re-training of the network. Simulation results show the interaction between rule-based and network-based system components during various phases of training and supervision.
ARTIFICIAL NEURAL NETWORK BASED INTELLIGENT SYSTEM
In information technology, a neural network is a system of programs and data structures that approximates the operation of the human brain. A neural network usually involves a large number of processors operating in parallel, each with its own small sphere of knowledge and access to data in its local memory. Typically, a neural network is initially "trained" or fed large amounts of data and rules about data relationships .A program can then tell the network how to behave in response to an external stimulus .For example, to input from a computer user who is interacting with the network or can initiate activity on its own within the limits of its access to the external world. The main features of this paper involves The basic idea of what is " A neural network ". The tools used in it. The three main applications of this phenomenon in the real time world. The first application includes the using of neural networks for the visual perception. The project of Receptive-Field Laterally Interconnected Synergetically Self-Organizing Map (RF-LISSOM) model of the primary visual cortex is explained. Secondly, the usage of neural networks to Control of robotic arms which works on their own in the industries. Finally, the Speech Recognition Using Neural Networks for Spoken Language Understanding. Everything from handwriting and speech recognition to stock market prediction will become more sophisticated as researchers develop better training methods and network architectures. The continuing advances in computer technology allow for the invention of ever more complex networks, eventually allowing us to exceed even the complexity of the human mind.
Neural networks as expert systems
Expert Systems with Applications, 1993
This paper shows that the high level decision-making function of expert systems, that depend upon man)' levels of logic, can be implemented in a neural network without the engineering of a detailed knowledge structure. Further, the neural network can interpolate and extrapolate a discrete set of associated input and output vectors, so that the output decision space is continuous. A drawback to the use of neural networks for decision making is that their training is universally problematic. We simplf.v the process nqth a new random optimization algorithm that consists of a global stage and a local stage. Unlike other methods, we also optimize the exponential rise parameters a and [3 in the sigmoids at the middle and output layers, which increases the speed of learning and decreases the minimum sum squared error.
Integrating (rules, neural networks) and cases for knowledge representation and reasoning in expert systems
Expert Systems with Applications, 2004
In this paper, we present an approach that integrates symbolic rules, neural networks and cases. To achieve it, we integrate a kind of hybrid rules, called neurules, with cases. Neurules integrate symbolic rules with the Adaline neural unit. In the integration, neurules are used to index cases representing their exceptions. In this way, the accuracy of the neurules is improved. On the other hand, due to neurule-based efficient inference mechanism, conclusions can be reached more efficiently. In addition, neurule-based inferences can be performed even if some of the inputs are unknown, in contrast to symbolic rule-based inferences. Furthermore, an existing symbolic rule-base with indexed exception cases can be converted into a neurule-base with corresponding indexed exception cases. Finally, empirical data can be used as a knowledge source, which facilitates knowledge acquisition. We also present a new high-level categorization of the approaches integrating rule-based and case-based reasoning. q
Neuro-Symbolic Approaches for Knowledge Representation in Expert Systems
International Journal of Hybrid Intelligent Systems, 2005
In this paper, we first present and compare existing categorization schemes for neuro-symbolic approaches. We then stress the point that not all hybrid neuro-symbolic approaches can be accommodated by existing categories. Such a case is rule-based neuro-symbolic approaches that propose a unified knowledge representation scheme suitable for use in expert systems. That kind of integrated schemes have the two component approaches tightly and indistinguishably integrated, offer an interactive inference engine and can provide explanations. Therefore, we introduce a new category of neuro-symbolic integrations, namely 'representational integrations'. Furthermore, two sub-categories of representational integrations are distinguished, based on which of the two component approaches of the integrations is given pre-eminence. Representative approaches as well as advantages and disadvantages of both sub-categories are discussed.
Applications of neural computing in the twenty-first century and 21 years of Neural Computing & Applications
Neural Computing and Applications, 2013
Neural networks to solve modern artificial intelligence tasks
Journal of Physics: Conference Series, 2019
Today the information technologies are increasing and improving by leaps and bounds becoming smarter and faster. And artificial intelligence is penetrating deeper and deeper in everyday life. So, artificial intelligence field is becoming more interesting for modern scientists and engineers. Artificial neural networks are used all over the world as one of the approaches that can provide with the high relevance level of resolving results of badly formalized or unformalized tasks. In this article a review of artificial neural networks development tools of different kinds is presented. Also there is a detailed description of all of them and the main features are emphasized. As a result, the table is presented that allows to make a quick compare of described tools and decide if any of them are suitable for a reader or not.
Analyses of the method development of decisions in an expert system with the use of information from an artificial neural network
MATEC Web of Conferences
The present article covers the use of an artificial intelligence system in the organization of the prevention of technical objects. For this purpose, the composition of this system including an intelligent diagnostic system and an intelligent maintenance system was characterized and described. An artificial neural network and an expert system, which work among others on the basis of the information developed by the neural network, perform a special function in these systems. It was mentioned in the article that the mathematical model of the regeneration process of the functional properties (prevention) of an object forms the basis of the organization of the prevention activities of technical devices and objects with the use of intelligent systems. This model demonstrated the possibilities and directions for the use of maintenance intelligent systems.
Application of Neural Networks for Assisting, Controlling and Designing Technological Processes
Proceedings of 10th World Congress on Computational Mechanics, 2014
The methodology and examples of utilising neural networks for assisting, controlling and designing technological processes is presented in the paper. Examples concern the continuous melting, casting and rolling of aluminium and alloy rod intended for drawing for electrical wires. Neural networks allow to build dependencies between the performance parameter set, chemical composition of the processed material, and product properties. These functions are used for the process control and for determining the optimal work point of the technological line. The initial data constitute the analytical results of the metal chemical composition and process data collected by the specially built system of canvassing realisation parameters of all unit processes. The effectiveness and efficiency of the system was assessed.
Artificial Intelligence And Expert Systems
The book is aimed at bringing out a comprehensive presentation of Artificial Intelligence (AI) based methodologies and software tools wherein, for the first time, the focus is on addressing a wide spectrum of problems in engineering.
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.