An expert network for DNA sequence analysis

The traditional fault diagnosis expert system is dependent on knowledge acquisition of the experts. Knowledge acquisition is recognized as the problem of expert system. In addition, there are also some limitations of adaptive capacity, learning ability and real-time. And artificial neural network with good fault-tolerance and associative memory function, as well as very strong self-adaptive, self-learning ability, just can make up for the limitations of traditional expert system. This paper will construct a new expert system with the artificial neural network into and fault tree. Besides fault tree and neural network, this article mainly introduces the system model of fault diagnosis of the fire control computer and sensor subsystem, the method and process of fault diagnosis. In this expert system, we use the object-oriented production rule to represent the knowledge, which solves the bottleneck problem of the diagnostic knowledge acquisition effectively. The inferential process begins with the abnormal event and finally finds all of the possible faults and the faulty component. For some possible faulty components, which have large number of fault samples, the neural network model can be used to diagnose. The training network of fault samples employs the BP neural network. Finally, simulation training results show that the fault diagnosis expert system based on the combination of fault tree and neural network is rational and effective in fault diagnosis of the fire control system, realizes perfectly the combination of new knowledge and old one, and can grasp the state of systems dynamically.

For a complicated system based on high technology, once a part breaks down, the entire system can not work normally. Moreover, due to the complexity of its structure and fault causes, the fault diagnosis of the system is also complex and indeterminate, a single test equipment can hardly finish a difficult diagnose task, and fault diagnosis expert system can resolve these problems effectively. The traditional diagnose expert systems have many problems such as the bottleneck of knowledge acquisition, the fragility of knowledge, the pool ability of self-study, the inefficient reasoning, and the monotonicity of reasoning, so there are certain limitations. But the artifical neural networks technology is a new system, it is an mathematical model that applies the structure like the joint of synapses in hypothalamic neurons, which has the strong ability to study, and can learn from samples, obtain knowledge, store it in the network in the form of weight and threshold, and it is easy to implement the parallel processing, has the character of association memory, own the better robust. it ability of adaptive self-study is manifested mainly in adjusting the weight of network according to the change of enviroment by learning algorithms, so as to adapt to the environmental change. But the neural network can not explain its own reasoning. Therefore we will apply the neural network to the expert knowledge system, which can make them learn each other's good points mutually for common progress, constructing the new neural network expert system. The system is applied to the power fault diagnosis, achieving good results.

This paper investigates a fault diagnosis expert system base on artificial neural network (ANN-FD-ES) for mixed-signal circuits. We built this system by given a faulty circuit. Based on the transient response testing (TRT) on the mixed-signal circuits, both analog and digital signal characteristics are unified. The system includes knowledge base, reasoning machine, explanatory, and interface. The diagnostic portion of the system is based on neural-calculation approach, which is used to establish the knowledge base composed of the weigh values and threshold values. With this approach, conclusions are developed directly from the outputs of the net, rather than the domain knowledge in traditional expert system. Compared with the traditional expert system, this system is good at processing data. The experimental results show that the system not only improves the shortcoming of the traditional expert system such as the deficiency on knowledge acquisition and self-learning capability, but also achieves a satisfied fault diagnosis efficiency.

Fault is defined as degradation between 100% performance and complete failure. The authors demonstrate how an artificial neural network can detect and diagnose faults from online process data. A wide range of input patterns can be learned by artificial neural networks in the presence of noise by changing the interconnections of the nodes, their thresholds for activation, and their individual weights. Artificial neural networks are able to take inputs from the processes without knowing the process model, allowing representation of complex engineering systems which are difficult to model either with traditional model-based engineering or knowledge-based expert systems. A description is given of some of the characters of a neural network that are useful for fault discrimination in a chemical plant. It is shown that even when using noisy sensor data, the misclassification rate is nil

Artificial intelligence (AI) techniques using sophisticated computer software are increasingly being used in the analysis of biological systems and for the design of biological models. The term artificial intelligence refers to machine-based processes that are functionally equivalent to human cognitive tasks for analyzing data in the domain for which they were designed. They are considered intelligent because of human-like reasoning processes. The expert system and the artificial neural network are two examples of such techniques (1). We are in the process of adapting these new computer information processing techniques into a knowledge-based modelling system which will be used to selectively search, analyze, retrieve and display thermoregulatory data in response to operator requests. We have developed a prototype system in which a rule-based expert system integrates its knowledge base with our accumulated historical database of experimentally obtained information on thermoregulation to provide new retrieval and analysis capabilities which will be useful for both research and teaching. This enables you to address otherwise unmanageable problems. Heuristics are often used. These types of systems are able to work with uncertainty, reason about what is unknown, and handle multivalued information with multiple conclusions. They are very effective at handling the problems that they were designed to solve, and are excellent for situations in which many people require access to a body of knowledge. Expert systems or neural networks when used alone are not optimal. The deficits in each can be filled by designing an integrated hybrid system in which neural networks are embedded within the rule based expert system and so both synergistically fill the gaps in each others technology.

It is an urgent task to implement a lot of expert systems to capture the valuable expertise of experienced doctors of traditional Chinese medicine. In order to meet the needs, a software tool is developed. It features a unified diagnosis model, a specially designed knowledge representation language and an efficient but effective inference engine. To implement an expert system, it is only necessary to input the expert’s knowledge expressed in knowledge representation language without the design of any additional software. The time and effort required for implementing an expert system are thus greatly saved. The software is very compact and can run on microcomputers e.g. IBM-PC/XT. Two traditional Chinese medical expert systems have been successfully implemented with the tool.

Computer systems based on artificial neural networks, often known as neural networks or neural nets, are modeled after the organic neural networks seen in animal brains. Artificial neurons are a group of interconnected units or nodes that serve as the foundation of an ANN and are meant to approximate the function of biological brain neurons. This article contains a survey of practical uses for neural networks. It offers a taxonomy of Artificial Neural Networks (ANNs), in-forms the reader of recent and upcoming developments in ANN applications research, and highlights research areas of interest. This paper also discusses the difficulties, contributions, comparative effectiveness, and important methodologies in ANN applications. The study examines a wide range of ANN applications in numerous disciplines, including computing, science, engineering, medicine, the environment, agriculture, mining, technology, climate, business, and the arts similar to nanotechnology. This study analyses performance assesses ANN contributions and criticizes techniques. The study discovered that artificial neural networks with feedforward and feedback propagation do well when applied to solving human problems. Therefore, based on data analysis characteristics such as accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and efficiency, we presented feed-forward and feedback propagation ANN models for research centers. A computational model known as an artificial neural network (ANN) is composed of many processing elements that accept inputs and produce outputs by their specified activation functions. This article will make other articles in this computer magazine easier to grasp for those who know little or nothing about ANNs. We go over the reasons for creating ANNs, the fundamentals of a biological neuron and an artificial computer model, network designs, learning mechanisms, and some of the most widely used ANN models. A successful ANN character recognition application brings us to a close. EDAS Evaluation Based on Distance from Average Solution method for Notebook(n1), Notebook(n2), Notebook(n3), Notebook(n4), Notebook(n5). Notebook (n1), Notebook (n2), Notebook (n3), Notebook (n4), Notebook (n5). Speed (MHz), RAM (Mbytes), Display (inches), Price (Euro). Notebook (n5) has the highest rank whereas Notebook (n1) has the lowest rank. The need for hybrid systems, Optical Neural Networks, EDAS Method.

There are only a limited number of computer-based systems designed to support anesthesiologists in the operating room. This is evident from the very small number of publications on this topic. These systems may be classified by the functions they perform and include: intelligent anesthesia workstations (with current data acquisition, conditioning and analysis subsystems), systems to detect critical conditions in patients (including expert systems in smart alarm capacity), anesthesia management systems (for planning and management) and drug administration systems. Drug administration systems may be subdivided into open-loop and closed-loop systems. The techniques applied for design of such systems vary extensively. There are traditional rule-based expert systems and probability-based systems, and more recently developed artificial intelligence methods, such as neural networks and fuzzy logic. Computers are valuable tools that have the potential to assist anesthesiologists in carrying out cumbersome and monotonous processes. Future efforts may result in the development of sophisticated systems capable of assuming more responsibilities and of reducing human workload and stress.

The Brain Emotional Learning (BEL) is a novel bio-inspired machine learning approach mentioned as a new class of artificial neural network (ANN). The artificial emotional neural network (AENN) is one of the BEL methods which used the genetic algorithm (GA) to compute proper weights, weights of the amygdala (AMYG), orbitofrontal cortex (OFC) weights and a bias value. AENN trained by GA has been reported that it could produce low error rates. However, AENN still has more rooms to enhance its prediction of performance, especially generalization and the prediction of performance. Therefore, this paper aims to propose a new training method for AENN. The JAYA optimization algorithm optimized the weights and the biases of AENN. Two new proposed models are named as AENN-Max-JAYA and AENN-Mean-JAYA. Their names are according to the way of selecting the additional expanded feature which obtained through either the max or the average of input patterns, respectively. From the experimental results for wind power forecasting dataset, the proposed methods proved that the results are better in generalization performance and give lower error rates which compared to the comparative AENN models and traditional ANNs.

An integrated knowledge-based system, RAISON, was developed for environmental applications, using expert system and neural network technologies. Practical considerations dictate that access and linkage with databases, spreadsheet, maps, documents, graphics and other modules are essential. It was found necessary to implement the expert system and neural network modules in the same software package, sharing common databases and a macro programming language to facilitate cross-linkage in hybrid systems. Some experiences with both techniques for solving acid rain problems, as well as the conversion from microcomputer to the workstation platform, are discussed. Comparison of results with independent data sets is found to be about 20 to 25% in error which is well within the measurement uncertainties. >

AFTER MORE THAN TWO DECADES OF NEGLECT, artificial neural networks (ANNs) are emerging as a strong contender in artificial intelligence. However, researchers whose leanings are more toward expert systems or other types of symbolic formalisms for knowledge representations warn against overly enthusiastic claims depicting ANNs as replicating the process of human thinking. While some of these objections are justified, the technology warrants a closer look by designers of educational software. Though neural networks are usually considered as a part of artificial intelligence, they are very different from the AI formalisms to which we’re accustomed. Expert systems use if-then rules to arrive at an answer. These rules are formulated by a knowledge engineer who works closely with a content expert to derive a model of the problem space. Neural networks, however, are fed sample cases (called “training sets≓), learn how to recognize patterns in the sample data, and then use this experience in handling new cases. Because neural nets are trained rather than programmed, the only overhead — other than the initial design — is in collecting and formatting the data sets (Carlson, 1990b, p. 50). This paper first describes the essentials of how an ANN learns, using a classic application in pattern recognition to demonstrate an operating ANN. Of the several “cognitive≓ roles ANNs are suitable for, two seem especially appropriate for computer-mediated education: (1) intelligent filters and (2) spreading, associative memories. To illustrate each, I describe prototypes I’m working on at the Air Force Armstrong Laboratory.

Due to the complex structure and massive volume of large equipment in petrochemical enterprises, it was very difficult to carry out inspection and maintenance work. The traditional expert system used a single knowledge base and reasoning machine, so the processing efficiency and prediction accuracy were low. In this paper, the dimensionality reduction which is used to process a large amount of physical information collected by the sensors, remove redundant components and extract main characteristic parameters. Meanwhile, combined with the powerful pattern recognition and judgment ability of fuzzy neural network, the forward reasoning mechanism and error back propagation keeping continuous training correction until meeting the accuracy requirements, the expert system gave the equipment fault prediction conclusion. Finally, taking main fan oil station circulating oil pump and motor of heavy oil catalytic cracking unit and nine stages of bearing damage as monitoring objects, the fault predictions was implemented by the following steps: data collection, feature extraction, dimension reduction of big data and expert system with the fusion of fuzzy neural network, and it is proved that the expert system with fuzzy neural network based on dimension reduction can greatly improve the convergence speed.

Nowadays , nonlinear time series and artificial neural networks (ANN) models are used for forecasting in the field of business, agriculture and soon. Recent studies have shown, ANN have been successfully used for forecasting of financial and agriculture data series The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and outputs. ANN have more advantages that can approximate to model both linear and nonlinear structures in time series, they are not able to handling both structures equally well. The autoregressive integrated moving average (ARIMA) model and two ANN models namely, Radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) methods were applied to Hyderabad airport traffic data. The data obtained for 15 years from 2002–2003 to 2016–2017 about domestic and international passenger of International Airport of Hyderabad, India. In this research paper, we compared the performances of ARIMA, RBFNN and ERNN were based on three measures: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results showed that RBFNN obtained the smallest MAE, MAPE and RMSE in both the modeling and forecasting processes. The performances of the three models ranked in ascending order were: ARIMA, ERNN and the RBFNN model. Keywords: T ime series, forecasting, artificial neural networks, ARIMA models, radial basis function neural networks, and Elman recurrent neural networks Cite this Article R. Ramakrishna, Berhe Aregay, Tewodros Gebregergs. The Comparison in Time Series Forecasting of Air Traffic Data by Autoregressive Integrated Moving Average Model, Radial Basis Function and Elman Recurrent Neural Networks. Research & Reviews: Journal of Statistics . 2018; 7(3): 75–90p.

This work focuses on the construction of an expert system shell for airborne equipment design. Traditional expert systems are constructed using a single monolithic software program for a specific application. Since the airborne equipment design is critical & complex, it demands the use of expert system technology. Since present aircrafts take in different equipments for different purposes, it is not feasible to think in terms of independently developed monolithic expert system program for each equipment. To overcome these problems, we have designed and developed a complex competent system i.e., a generic component based expert system for airborne equipment design. The main advantage of the component-based approach is that, the knowledge base is not hard coded and these components can be coupled with any domain-specific knowledge bases. The expert system with its expertise knowledge is made to guide the designer by providing design guidelines & testing procedures for the desired equipment. The expert system also audits the design and provides guidelines to modify & to improve the design.

This paper presents an investigation of an artificial neural network (ANN) to predict the flutter characteristics of a simple wing that under went multiple weight variation changes. The weight variations were used to simulate the addition of non-structural weight or structural repair weight. The test case wing had 24 designated repair areas where repair weights were added as lumped masses. Weight and location of the repairs were randomly generated to increase the sample size and help the generalization of the ANN. The ANN was evaluated for both the flutter speed and flutter frequency for multiple levels of structural damping. The number of test cases required to train the neural network was also evaluated for both the error between the input and output results as well as the generalization of the neural networks.