Development of automated test system for diesel engines based on fuzzy logic

© 2016 IEEE.To control a diesel engine during testing, the principles of fuzzy output, which are widely used in fuzzy-logic controller development, could be applied. The controller's main task is to monitor an external object, in which case the behavior of the monitored object is described with the fuzzy rules. The most important application area of the fuzzy set theory is the fuzzy logic controllers. Their operation slightly differs from the operation of common controllers. In order to describe the system, the expert knowledge is used instead of differential equations. Control of the automation systems for engine testing (AST) with the fuzzy-logic controller should be based on a knowledge database with fuzzy rules. Such database could be created with expert knowledge, neural network, or direct measuring method. Development of an adaptive control system for diesel engine testing process based on the fuzzy logic enables simplification of the system's structural components and provision of discrete control procedure with some uninterruptible properties, which could improve the control and reduce the scope of the knowledge database. Fuzzy logic makes it simple to input a priory information about an object in the form of fuzzy control rules into the adaptive control system. Similarity of form and natural language relatively easy allows obtaining necessary expert knowledge. A prior information provides one of the key initial conditions of the system developed according to adaptive control method-the condition of supreme initial adaptation

Measuring the Score Matching of the Pairwise Deoxyribonucleic Acid Sequencing using Neuro-Fuzzy

The proposed model for getting the score matching of the deoxyribonucleic acid (DNA) sequence is introduced; the Neuro-Fuzzy procedure is the strategy actualized in this paper; it is used the collection of biological information of the DNA sequence performing with global and local calculations so as to advance the ideal arrangement; we utilize the pairwise DNA sequence alignment to gauge the score of the likeness, which depend on information gathering from the pairwise DNA series to be embedded into the implicit framework; an adaptive neuro-fuzzy inference system model is reasonable for foreseeing the matching score through the preparation and testing in neural system and the induction fuzzy system in fuzzy logic that accomplishes the outcome in elite execution

Модель адаптивного тестування з нечіткою логікою

Розглядається адаптивна модель тестування, що використовує апарат нечіткої логіки. Пропонується опис нечітких характеристик тестових завдань і функцій визначення рівня підготовки за 12-бальною шкалою. На основі даного опису будується алгоритм тестування з можливістю гнучкого настроювання викладачем-експертом.Рассматривается адаптивная модель тестирования, использующая аппарат нечеткой логики. Предлагается описание нечетких характеристик тестовых заданий и функций определения уровня подготовки по 12- балльной шкале. На основании данного описания строится алгоритм тестирования с возможностью гибкой настройки преподавателем-экспертом.There is considered the model of adaptive of testing that is using device of fuzzy logic. There is offered description of the fuzzy characteristics of test tasks and functions of definition of a support level on 12-ball scale. The algorithm of testing with an opportunity of flexible adjustment by the teacher-expert is constructed on the basis of the given description

Fuzzy/Neural Software Estimates Costs of Rocket-Engine Tests

The Highly Accurate Cost Estimating Model (HACEM) is a software system for estimating the costs of testing rocket engines and components at Stennis Space Center. HACEM is built on a foundation of adaptive-network-based fuzzy inference systems (ANFIS) a hybrid software concept that combines the adaptive capabilities of neural networks with the ease of development and additional benefits of fuzzy-logic-based systems. In ANFIS, fuzzy inference systems are trained by use of neural networks. HACEM includes selectable subsystems that utilize various numbers and types of inputs, various numbers of fuzzy membership functions, and various input-preprocessing techniques. The inputs to HACEM are parameters of specific tests or series of tests. These parameters include test type (component or engine test), number and duration of tests, and thrust level(s) (in the case of engine tests). The ANFIS in HACEM are trained by use of sets of these parameters, along with costs of past tests. Thereafter, the user feeds HACEM a simple input text file that contains the parameters of a planned test or series of tests, the user selects the desired HACEM subsystem, and the subsystem processes the parameters into an estimate of cost(s)

Development of an adaptive fuzzy logic controller for HVAC system

An adaptive approach to control a cooling coil chilled water valve operation, called adaptive fuzzy logic control (AFLC), is developed and validated in this study. The AFLC calculates the error between the supply air temperature and supply air temperature set point for air in an air handling unit (AHU) of a heating, ventilating, and air conditioning (HVAC) system and determines optimal fuzzy rule matrix to minimize the hydronic energy consumption while maintaining occupant comfort. The AFLC uses genetic algorithms and evolutionary strategies to determine the fuzzy rule matrix and fuzzy membership functions for an AHU in HVAC systems;Cooling coil models are developed using neural network, general regression neural network and lump capacitance methods to predict the supply air temperature. These models helped with the development of the adaptive fuzzy logic controller;Two types of validation experiments were conducted, one with cyclically changing supply air temperatures and the second with cyclically changing supply air flow rates. Experiments conducted on two identical real HVAC systems were used to compare the performances of the AFLC to a conventional proportional, integral and derivative (PID) controller. To remove bias between the testing systems, the controllers were switched from one system to the other;The validation experiments indicate that the HVAC system operated under the AFLC consumes 1 to 7 % less hydronic energy when compared with a conventional PID controlled system. More actuator travel distance was observed when using the AFLC. The AFLC maintained better occupant comfort conditions when compared with the conventional PID controller. It was observed that the controlled variable for the AFLC system required 0 to 185% more rise time, had 9 to 68% less overshoot and required 11 to 45% less settling time as compared to the conventional PID controlled system

A Soft Computing Approach to Dynamic Load Balancing in 3GPP LTE

A major objective of the 3GPP LTE standard is the provision of high-speed data services. These services must be guaranteed under varying radio propagation conditions, to stochastically distributed mobile users. A necessity for determining and regulating the traffic load of eNodeBs naturally ensues. Load balancing is a self-optimization operation of self-organizing networks (SON). It aims at ensuring an equitable distribution of users in the network. This translates into better user satisfaction and a more efficient use of network resources. Several methods for load balancing have been proposed. Most of the algorithms are based on hard (traditional) computing which does not utilize the tolerance for precision of load balancing. This paper proposes the use of soft computing, precisely adaptive Neuro-fuzzy inference system (ANFIS) model for dynamic QoS aware load balancing in 3GPP LTE. The use of ANFIS offers learning capability of neural network and knowledge representation of fuzzy logic for a load balancing solution that is cost effective and closer to human intuitio

Adaptive Resonance Theory: Self-Organizing Networks for Stable Learning, Recognition, and Prediction

Adaptive Resonance Theory (ART) is a neural theory of human and primate information processing and of adaptive pattern recognition and prediction for technology. Biological applications to attentive learning of visual recognition categories by inferotemporal cortex and hippocampal system, medial temporal amnesia, corticogeniculate synchronization, auditory streaming, speech recognition, and eye movement control are noted. ARTMAP systems for technology integrate neural networks, fuzzy logic, and expert production systems to carry out both unsupervised and supervised learning. Fast and slow learning are both stable response to large non stationary databases. Match tracking search conjointly maximizes learned compression while minimizing predictive error. Spatial and temporal evidence accumulation improve accuracy in 3-D object recognition. Other applications are noted.Office of Naval Research (N00014-95-I-0657, N00014-95-1-0409, N00014-92-J-1309, N00014-92-J4015); National Science Foundation (IRI-94-1659

Learning, Categorization, Rule Formation, and Prediction by Fuzzy Neural Networks

National Science Foundation (IRI 94-01659); Office of Naval Research (N00014-91-J-4100, N00014-92-J-4015) Air Force Office of Scientific Research (90-0083, N00014-92-J-4015