Diagnostic system with an artificial neural network which determines a diagnostic information for the servicing of a reparable technical object

In this paper, the features of a diagnostic measurement setup have been determined. The purpose of the setup was registration of diagnostic signals taken from an electronically controlled gasoline-powered engine. Special attention has been paid to the presentation of the features of an analog–digital converter card used, as well as to the possibility of its practical use. Another important aspect is the design and programming of computer software dedicated for action along with the setup. The results of the work have been presented using the example of the engine’s microprocessor control module. A diagnostic analysis was conducted, as a result of which sets of the functional elements of the object and its diagnostic signals were determined. Also, the methodology of the diagnostic examination of the technical system was presented. The result was a functional and diagnostic model, which constituted the basis for initial diagnostic information, which is provided by the sets of information concerning the elements of the basic modules and their output signals.     

Diagnostic system with an artificial neural network in diagnostics of an analogue technical object

This paper presents a method to control an operation process of a complex technical object, for example a car engine, with the use of trivalent diagnostic information. Also, a general diagram of the complex technical object was presented, and its internal structure was described. A diagnostic analysis was conducted, as a result of which sets of the functional elements of the object and its diagnostic signals were determined. Also, the methodology of the diagnostic examination of the technical system was presented. The result was a functional and diagnostic model, which constituted the basis for initial diagnostic information, which is provided by the sets of information concerning the elements of the basic modules and their output signals. The article also covers a diagnostic system which uses a DIAG computer programme for the recognition of the states of technical objects in trivalent logics. A programme was presented and described in an analytical form for diagnosis through an artificial neural network (ANN), which recognises the states of reparable technical objects in trivalent logics. The final results obtained from the computations conducted by the DIAG programme are presented in the table of the states of the object.     

Investigation of the operation process of a repairable technical object in an expert servicing system with an artificial neural network

The paper presents a system for the diagnosis of repairable technical objects with the use of an artificial neural network of a radial basis function (RBF) type. The structure and the algorithm of the work of an RBF type neural network are described. This paper presents a method to control an operation process of a complex technical object with the use of trivalent diagnostic information. Also, a general diagram of the complex technical object was presented, and its internal structure was described. A diagnostic analysis was conducted, as a result of which the sets of the functional elements of the object and its diagnostic signals were determined. Also, the methodology of the diagnostic examination of the technical system was presented. The result was a functional and diagnostic model, which constituted the basis for initial diagnostic information which is provided by the sets of information concerning the elements of the basic modules and their output signals. The final results obtained for the computations conducted by the DIAG programme were presented in the table of the states of the object.     

Diagnostic system for the diagnosis of a reparable technical object, with the use of an artificial neural network of RBF type

The paper presents a system for the diagnosis of repairable technical objects with the use of an artificial neural network of a radial basis function (RBF) type. The structure and the algorithm of the work of an RBF type neural network are described. This paper presents a method to control an operation process of a complex technical object with the use of trivalent diagnostic information. Also, a general diagram of the complex technical object was presented, and its internal structure was described. A diagnostic analysis was conducted, as a result of which the sets of the functional elements of the object and its diagnostic signals were determined. Also, the methodology of the diagnostic examination of the technical system was presented. The result was a functional and diagnostic model, which constituted the basis for initial diagnostic information which is provided by the sets of information concerning the elements of the basic modules and their output signals. The final results obtained for the computations conducted by the DIAG programme were presented in the table of the states of the object.     

Taking decisions in the expert intelligent system to support maintenance of a technical object on the basis information from an artificial neural network

In this paper, an intelligent operation system, which consists of an intelligent diagnostic subsystem (with a neural network) and an intelligent maintenance subsystem (with an expert system), was presented and discussed. The artificial neural network and the expert system, which use the information developed in the neural network, perform a special function in this system. The functional combination of the artificial neural network and the expert system together created a new solution in the form of an intelligent system, which was referred to as an intelligent maintenance system. This article also covers decision-making methods that are used in an expert maintenance system and whose purpose is an organization and control of the process of the prevention of technical objects. For this purpose, the method was described of taking decisions by an expert for complex parametric type hypotheses and for simple finished type hypotheses in the set of possible decisions’ hypotheses. A considerable part of this paper covers the presentation of the method to transform diagnostic information into the required form of maintenance information. For this purpose, an algorithm of the work of maintenance system was performed and descried. In the creation process of the maintenance knowledge base, the specialist knowledge of a human specialist was also used. Hence, a skilful and proper taking of decisions by an expert to create this set of information is essential. Two inference methods were characterized and described in this paper. The theoretical results obtained were verified in the examination of the influence of each of these decision-making inference methods on the final results of the process of the prevention treatment of an object.     

Assessment of the quality of decisions worked out by an artificial neural network (DIAG) which diagnoses a technical object

The issues of the examination and determination of the ambiguity of the identification of the states of a technical object in a diagnostic system with an artificial neural network are presented in this article. It is an important problem in the operation of each inference (decision) system, in which various types of decisions are worked out, particularly including ones for diagnostic systems. For this purpose, a diagnostic system including its elements, in which an artificial neural network is used, is characterized and described. The structure together with the algorithm of a diagnostic neural network is presented. A diagram was drawn up, and the circulation of information in the diagnostic system was described in the perspective of errors brought into decision information by the individual elements of this system. A formula for the general error during working out of decisions in the system is put forward. It was also indicated that a number of factors including interferences and influence of the environment, errors during the measurement of the values of the properties of diagnostic signals, errors at the determination of the ranges of possible (permissible and limiting) changes of the properties of diagnostic signals defined in the inference rules and the accuracy of the drawing up of diagnostic inference rules have all a direct impact on the ambiguity level of the identification of the states of a technical object. In the present article, it is also the possibilities that are put forward for the determination and optimization of the ambiguity of the identification of the states of a technical object. For this purpose, the function that determines the quality of the identification and non-identification of the object’s state was defined in this article. A practical assessment method of the ambiguity level of the identification of the states of a technical object in the examined diagnostic system is additionally presented in an example with the use of a radar system.     

人工神经网络在ERP系统中的应用

在传统的ERP的基础上,增加专家系统模块,即基于人工神经网络技术的预测分析模块,提出了ERP和专家系统的集成管理方法,完成复杂的非线性预测,以使ERP系统智能化、自动化水平更高。该模块采用反向传输BP神经网络模型来实现,通过网络的自适应学习和训练,找出输入和输出之间的内在联系,以求解问题。利用该专家系统对汽车制造企业市场销售量进行预测,结果表明：该方法性能、实用性和通用性好。     

Application of an artificial immune system-based fuzzy neural network to a RFID-based positioning system

Due to the rapid development of globalization, which makes supply chain management more complicated, more companies are applying radio frequency identification (RFID), in warehouse management. The obvious advantages of RFID are its ability to scan at high-speed, its penetration and memory. In addition to recycling, use of a RFID system can also reduce business costs, by indentifying the position of goods and picking carts. This study proposes an artificial immune system (AIS)-based fuzzy neural network (FNN), to learn the relationship between the RFID signals and the picking cart’s position. Since the proposed network has the merits of both AIS and FNN, it is able to avoid falling into the local optimum and possesses a learning capability. The results of the evaluation of the model show that the proposed AIS-based FNN really can predict the picking cart position more precisely than conventional FNN and, unlike an artificial neural network, it is much easier to interpret the training results, since they are in the form of fuzzy IF–THEN rules.     

Development of a predictive system for car fuel consumption using an artificial neural network

A predictive system for car fuel consumption using a back-propagation neural network is proposed in this paper. The proposed system is constituted of three parts: information acquisition system, fuel consumption forecasting algorithm and performance evaluation. Although there are many factors which will effect the fuel consumption of a car in a practical drive procedure, however, in the present system the impact factors for fuel consumption are simply decided as make of car, engine style, weight of car, vehicle type and transmission system type which are used as input information for the neural network training and fuel consumption forecasting procedure. In the fuel consumption forecasting, to verify the effect of the proposed predictive system, an artificial neural network with back-propagation neural network has a learning capability for car fuel consumption prediction. The prediction results demonstrated that the proposed system using neural network is effective and the performance is satisfactory in fuel consumption prediction.     

Hardware implementation of an artificial neural network model to predict the energy production of a photovoltaic system

An artificial neural network trained using only the data of solar radiation presents a good solution to predict, in real time, the power produced by a photovoltaic system. Even though the neural network can run on a Personal Computer, it is expensive to have a control room with a Personal Computer for small photovoltaic installations. A FPGA running the neural network hardware will be faster and less expensive. In this work, to assist the hardware implementation of an artificial neural network with a FPGA, a specific tool was used: an Automatic General Purpose Neural Hardware Generator. This tool allows for an automatic configuration system that enables the user to configure the artificial neural network, releasing the user from the details of the physical implementation. The results show that it is possible to accurately model the photovoltaic installation based on data from a nearby meteorological installation and the hardware implementation produces low cost and precise results.     

Multi-class object detection system using hybrid convolutional neural network architecture

Multimedia Tools and Applications - Object detection in computer vision has been a significant research area for the past decade. Identifying objects with multiple classes from an image has...     

Application of an artificial neural network in canopy scattering inversion

Owing to their recent success in other inversion tasks, application of an artificial neural network to the development of an inversion algorithm for radar scattering from vegetation canopies is considered. Because canopy scattering models are complicated functions of the desired biophysical parameters (vegetation biomass, leaf area index, soil moisture content, etc.), the development of an effective inversion algorithm is not a straightforward task. The Michigan Microwave Canopy Scattering (MIMICS) model, which has shown remarkable success in predicting the radar response to vegetation canopies, was used, as were measured polarimetric backscatter values. Hence, the radiative transfer simulation code, MIMICS, was used to produce some of the training data. The inputs to the neural network were the expected polarimetric backscatter values from specific canopies, while the outputs were the desired parameters, such as tree heights, crown thickness, leaf density, etc.

Two special cases were examined: (1) inversion of MIMICS given modelled aspen stands of different ages; (2) inversion of measured data from the Duke forest loblolly pine stands. The MIMICS inversion shows that neural networks are capable of accurately inverting some of the parameters of such a complicated model. The implication is that once MIMICS is made to model the radar data for a specific application, then inversion of the radar data may be accomplished. The measured data inversion shows that, even without a model such as MIMICS, one may train a neural network to invert several parameters of interest. However, this depends on accurate and complete surveys of the ground truth data to be useful.     

Plane-Gaussian artificial neural network

Multilayer perceptrons (MLPs) and radial basis functions networks (RBFNs) have been widely concerned in recent years. In this paper, based on k-plane clustering (kPC) algorithm, we propose a novel artificial network model termed as Plane-Gaussian network to enlarge the arsenal of the neural networks. This network adopts a so-called Plane-Gaussian activation function (PGF) in hidden neurons. Replacing traditional central point of Gaussian radial basis function (RBF) with central hyperplane, PGF forms a band-shaped rather than spheral-shaped receptive field in RBF, which makes PGF able to express its peculiar geometrical characteristics: locality and globality. Importantly, it is also proved that PGF network (PGFN) having one hidden layer is capable of universal approximation. As a universal approximator, PGFN gives an informal way of bridging the gap between MLP and RBFN. The experiments report comparison between training time and classification accuracies on some artificial and UCI datasets and conclude that (1) PGFN runs significantly faster than MLP and (2) PGFN has comparable or better classification performance than MLP and RBFN, especially in subspace-distributed datasets.