• Title/Summary/Keyword: Back-propagation network

Search Result 41, Processing Time 0.088 seconds

A Neural Net System Self-organizing the Distributed Concepts for Speech Recognition (음성인식을 위한 분산개념을 자율조직하는 신경회로망시스템)

  • Kim, Sung-Suk;Lee, Tai-Ho
    • Journal of the Korean Institute of Telematics and Electronics
    • /
    • v.26 no.5
    • /
    • pp.85-91
    • /
    • 1989
  • In this paper, we propose a neural net system for speech recognition, which is composed of two neural networks. Firstly the self-supervised BP(Back Propagation) network generates the distributed concept corresponding to the activity pattern in the hidden units. And then the self-organizing neural network forms a concept map which directly displays the similarity relations between concepts. By doing the above, the difficulty in learning the conventional BP network is solved and the weak side of BP falling into a pattern matcher is gone, while the strong point of generating the various internal representations is used. And we have obtained the concept map which is more orderly than the Kohonen's SOFM. The proposed neural net system needs not any special preprocessing and has a self-learning ability.

  • PDF

A Study on fault Detection of Off-design Performance for Smart UAV Propulsion System (스마트 무인기용 가스터빈 엔진의 탈설계 영역 구성품 손상 진단에 관한 연구)

  • Kong, Chang-Duk;Kho, Seong-Hee;Ki, Ja-Young;Lee, Chang-Ho
    • Journal of the Korean Society of Propulsion Engineers
    • /
    • v.11 no.3
    • /
    • pp.29-34
    • /
    • 2007
  • In this study a model-based diagnostic method using the Neural Network was proposed for PW206C turbo shaft engine and performance model was developed by SIMULINK. Fault and test database to build the NN was obtained at various off-design operating range such as flight altitude, flight Mach number and gas generator rotational speed variation. According to the fault detection analysis results, it was confirmed that the proposed fault detection method could find well the fault of compressor, compressor turbine and power turbine at on-design point as well as off-design point conditions.

A Study on Loose Part Monitoring System in Nuclear Power Plant Based on Neural Network (원전 금속파편시스템에 신경회로망 적용연구)

  • Kim, Jung-Soo;Hwang, In-Koo;Kim, Jung-Tak;Moon, Byung-Soo;Lyou, Joon
    • Proceedings of the Korean Institute of Intelligent Systems Conference
    • /
    • 2002.05a
    • /
    • pp.227-230
    • /
    • 2002
  • The Loose Part Monitoring System(LPMS) has been designed to detect, locate and evaluate detached or loosened parts and foreign objects in the reactor coolant system. In this paper, at first, we presents an application of the back propagation neural network. At the preprocessing step, the moving window average filter is adopted to reject the low frequency background noise components. And then, extracting the acoustic signature such as Starting point of impact signal, Rising time, Half period, and Global time, they are used as the inputs to neural network. Secondly, we applied the neural network algorithm to LPMS in order to estimate the mass of loose parts. We trained the impact test data of YGN3 using the backpropagation method. The input parameter for training is Rising Time, Half Period, Maximum amplitude. The result showed that the neural network would be applied to LPMS. Also, applying the neural network to the Practical false alarm data during startup and impact test signal at nuclear power Plant, the false alarms are reduced effectively. 1.

  • PDF

Prediction of Influent Flow Rate and Influent Components using Artificial Neural Network (ANN) (인공 신경망(ANN)에 의한 하수처리장의 유입 유량 및 유입 성분 농도의 예측)

  • Moon, Taesup;Choi, Jaehoon;Kim, Sunghui;Cha, Jaehwan;Yoom, Hoonsik;Kim, Changwon
    • Journal of Korean Society on Water Environment
    • /
    • v.24 no.1
    • /
    • pp.91-98
    • /
    • 2008
  • This work was performed to develop a model possible to predict the influent flow and influent components, which are one of main disturbances causing process problems at the operation of municipal wastewater treatment plant. In this study, artificial neural network (ANN) was used in order to develop a model that was able to predict the influent flow, $COD_{Mn}$, SS, TN 1 day-ahead, 2day-ahead and 3 day ahead. Multi-layer feed-forward back-propagation network was chosen as neural network type, and tanh-sigmoid function was used as activation function to transport signal at the neural network. And Levenberg-Marquart (LM) algorithm was used as learning algorithm to train neural network. Among 420 data sets except missing data, which were collected between 2005 and 2006 at field plant, 210 data sets were used for training, and other 210 data sets were used for validation. As result of it, ANN model for predicting the influent flow and components 1-3day ahead could be developed successfully. It is expected that this developed model can be practically used as follows: Detecting the fault related to effluent concentration that can be happened in the future by combining with other models to predict process performance in advance, and minimization of the process fault through the establishment of various control strategies based on the detection result.

Servo Control of Hydraulic Motor using Artificial Intelligence (인공지능을 이용한 유압모터의 서보제어)

  • 신위재;허태욱
    • Journal of the Institute of Convergence Signal Processing
    • /
    • v.4 no.3
    • /
    • pp.49-54
    • /
    • 2003
  • In this paper, we propose a controller with the self-organizing neural network compensator for compensating PID controller's response. PID controller has simple design method but needs a lot of trials and errors to determine coefficients. A neural network control method does not have optimal structure as the parameters are pre-specified by designers. In this paper, to solve this problem, we use a self-organizing neural network which has Back Propagation Network algorithm using a Gaussian Potential Function as an activation function of hidden layer nodes for compensating PID controller's output. Self-Organizing Neural Network's learning is proceeded by Gaussian Function's Mean, Variance and number which are automatically adjusted. As the results of simulation through the second order plant, we confirmed that the proposed controller get a good response compare with a PID controller. And we implemented the of controller performance hydraulic servo motor system using the DSP processor. Then we observed an experimental results.

  • PDF

Modeling of surface roughness in electro-discharge machining using artificial neural networks

  • Cavaleri, Liborio;Chatzarakis, George E.;Trapani, Fabio Di;Douvika, Maria G.;Roinos, Konstantinos;Vaxevanidis, Nikolaos M.;Asteris, Panagiotis G.
    • Advances in materials Research
    • /
    • v.6 no.2
    • /
    • pp.169-184
    • /
    • 2017
  • Electro-Discharge machining (EDM) is a thermal process comprising a complex metal removal mechanism. This method works by forming of a plasma channel between the tool and the workpiece electrodes leading to the melting and evaporation of the material to be removed. EDM is considered especially suitable for machining complex contours with high accuracy, as well as for materials that are not amenable to conventional removal methods. However, several phenomena can arise and adversely affect the surface integrity of EDMed workpieces. These have to be taken into account and studied in order to optimize the process. Recently, artificial neural networks (ANN) have emerged as a novel modeling technique that can provide reliable results and readily, be integrated into several technological areas. In this paper, we use an ANN, namely, the multi-layer perceptron and the back propagation network (BPNN) to predict the mean surface roughness of electro-discharge machined surfaces. The comparison of the derived results with experimental findings demonstrates the promising potential of using back propagation neural networks (BPNNs) for getting a reliable and robust approximation of the Surface Roughness of Electro-discharge Machined Components.

A Study on Performance Diagnostic of Smart UAV Gas Turbine Engine using Neural Network (신경회로망을 이용한 스마트 무인기용 가스터빈 엔진의 성능진단에 관한 연구)

  • Kong Chang-Duk;Ki Ja-Young;Lee Chang-Ho;Lee Seoung-Hyeon
    • Proceedings of the Korean Society of Propulsion Engineers Conference
    • /
    • 2006.05a
    • /
    • pp.213-217
    • /
    • 2006
  • An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV (Unmanned Aerial Vehicle) which has been developed by KARI (Korea Aerospace Research Institute). For teaming the NN, a BPN with one hidden, one input and one output layer was used. The input layer had seven neurons of variations of measurement parameters such as SHP, MF, P2, T2, P4, T4 and T5, and the output layer used 6 neurons of degradation ratios of flow capacities and efficiencies for compressor, compressor turbine and power turbine. Database for network teaming and test was constructed using a gas turbine performance simulation program. From application results for diagnostics of the PW206C turboshaft engine using the learned networks, it was confirmed that the proposed diagnostics algorithm could detect well the single fault types such as compressor fouling and compressor turbine erosion.

  • PDF

Applications of Artificial Neural Networks for Using High Performance Concrete (고성능 콘크리트의 활용을 위한 신경망의 적용)

  • Yang, Seung-Il;Yoon, Young-Soo;Lee, Seung-Hoon;Kim, Gyu-Dong
    • Journal of the Korean Society of Hazard Mitigation
    • /
    • v.3 no.4 s.11
    • /
    • pp.119-129
    • /
    • 2003
  • Concrete and steel are essential structural materials in the construction. But, concrete, different from steel, consists of many materials and is affected by many factors such as properties of materials, site environmental situations, and skill of constructors. Concrete have two kinds of properties, immediately knowing properties such as slump, air contents and time dependent one like strength. Therefore, concrete mixes depend on experiences of experts. However, at point of time using High Performance Concrete, new method is wanted because of more ingredients like mineral and chemical admixtures and lack of data. Artificial Neural Networks(ANN) are a mimic models of human brain to solve a complex nonlinear problem. They are powerful pattern recognizers and classifiers, also their computing abilities have been proven in the fields of prediction, estimation and pattern recognition. Here, among them, the back propagation network and radial basis function network ate used. Compositions of high-performance concrete mixes are eight components(water, cement, fine aggregate, coarse aggregate, fly ash, silica fume, superplasticizer and air-entrainer). Compressive strength, slump, and air contents are measured. The results show that neural networks are proper tools to minimize the uncertainties of the design of concrete mixtures.

Tuning Backpropagation Networks for Analyzing NIR Data

  • M.A.Hana;W.F.McClure;T.B.Whitaker
    • Near Infrared Analysis
    • /
    • v.2 no.1
    • /
    • pp.15-23
    • /
    • 2001
  • Designing (specifying the number of nodes in each layer) and training (calibration and validation) back-propagation (BP) for analyzing NIR data can be an arduous and time-consuming task. Actually, training is somewhat trivial. A BP network may be trained by randomly dividing the data set (DS) into two parts, training the network with one part and checking its performance with the other part. However, this procedure is plagued with the lack of objective information about network characteristics - the required number of nodes in the hidden layer(s) and the number of epochs needed to train for optimal performance. Work reported in this paper compares a BP network tuning procedure with a conventional reference (training and testing) procedure. The tuning procedure, believed to have several novel attributes, involved randomly dividing a data set into five groups. Each of the five groups was randomly subdivided into two groups with 80% in a training set and 20% in a tuning set. Training was interrupted periodically after every 100 epochs. During each interruption, network performance was checked against the tuning set - each time recording the mean-squared error (MSE) and the number of epochs (K) needed to reach this point. This procedure continued until a plot of MSE vs total epochs identified a minimum MSE. The number of epochs required achieve minimum MSE was noted. Now optimized (or tuned), network performance was determined by testing the network with all available data. One nice feature of using the tuning method is that the entire process can be executed without user input - i.e., the whole process of developing and training a BP network becomes objective. Four different near infrared data sets (A, B, C and D) were used in this work. Tow of the data sets (A and B) were used to determine the concentration of nicotine in tobacco samples. The other two sets (C and D) were used as a basis for classifyign tobaccos. The optimum BP architecture for each of the four data sets were those consisting of 1, 5, 2 and 1 hidden units in the hidden layer, respectively. The suggested tuning method improved, though marginally in some cases, the true performances of all calibration models as well as their standard deviations. since this work was dependent upon the artificial neural network (ANN) literature, a glossary of terms is given at the end of this paper. Results indicate improved performance using the tuning procedure. In addition, BP network calibrations were better than multiple linear regression (MLR) calibrations on the same data.

Implementation of A Multiple-agent System for Conference Calling (회의 소집을 위한 다중 에이전트 시스템의 구현)

  • 유재홍;노승진;성미영
    • Journal of Intelligence and Information Systems
    • /
    • v.8 no.2
    • /
    • pp.205-227
    • /
    • 2002
  • Our study is focused on a multiple-agent system to provide efficient collaborative work by automating the conference calling process with the help of intelligent agents. Automating the meeting scheduling requires a careful consideration of the individual official schedule as well as the privacy and personal preferences. Therefore, the automation of conference calling needs the distributed processing task where a separate calendar management process is associated for increasing the reliability and inherent parallelism. This paper describes in detail the design and implementation issues of a multiple-agent system for conference calling that allows the convener and participants to minimize their efforts in creating a meeting. Our system is based on the client-sewer model. In the sewer side, a scheduling agent, a negotiating agent, a personal information managing agent, a group information managing agent, a session managing agent, and a coordinating agent are operating. In the client side, an interface agent, a media agent, and a collaborating agent are operating. Agents use a standardized knowledge manipulation language to communicate amongst themselves. Communicating through a standardized knowledge manipulation language allows the system to overcome heterogeneity which is one of the most important problems in communication among agents for distributed collaborative computing. The agents of our system propose the dates on which as many participants as possible are available to attend the conference using the forward chaining algorithm and the back propagation network algorithm.

  • PDF