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检索条件"主题词=Back-propagation Algorithm"

共 209 条记录，以下是141-150 订阅

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Deformation Forecasting using a Hybrid Time Series and Neural Network Model

Deformation Forecasting using a Hybrid Time Series and Neura...

引用

2nd International Conference on Civil Engineering and Transportation (ICCET 2012)

作者： Wang, Qiang Gao, Ning Jiao, Wen Zhe Wang, Guan Jie China Univ Min & Technol Coll Geosci & Surveying Engn Beijing 100083 Peoples R China

ISBN: (纸本)9783037855652

In order to improve the accuracy and reliability of prediction of deformation monitoring data, a hybrid modeling and forecasting approach based on autoregressive model (AR) and the back-propagation (BP) neural network is proposed to forecast the deformation. The results of experiments show that this method can forecast the deformation precisely, and it is more suitable for those occasions where the deformation monitoring data should meet the high demand.

关键词： autoregressive model neural network back-propagation algorithm hybrid model deformation prediction

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WLAN location determination model based on the artificial neural networks

WLAN location determination model based on the artificial ne...

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47th International Symposium ELMAR-2005 on Multimedia Systems and Applications

作者： Vilovic, I Zovko-Cihlar, B Univ Dubrovnik Dept Elect Engn & Comp Dubrovnik Croatia

ISBN: (纸本)9537044041

Along with increasing popularity of wireless LAN, problem of location determination for mobile users becomes more important. The strengths of RF signals arriving from several access points can be used for location determination of the mobile terminal. In indoor environments the received signal level is very complex function of the distance. The solution can be found in the area of artificial neural networks. The neural networks can be learned to classify data. Labeled data examples of signal strengths at known locations must be collected by the measurement. This data will serve for the training of the network with appropriate training algorithm. The trained network is capable to determine location on the base of new signal strengths as a process of generalization. The advantage of the method is that it doesn't need any extra hardware, while with flexible neural network model achieves lower distance errors in determining position comparable with other methods. For successful position determination only what is needed are a map of indoor space and several identified locations to train the network.

关键词： WLAN artificial neural network location determination synaptic weights activation function back-propagation algorithm conjugate gradient algorithm

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Application of Artificial Neural Network to quantitative analysis of Raman spectrum

Application of Artificial Neural Network to quantitative ana...

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4th International Conference on Photonics and Imaging in Biology and Medicine

作者： Chen, Chen Zhang, Guoping Li, Gang Cent China Normal Univ Coll Phys Sci & Technol Wuhan 430079 Peoples R China

ISBN: (纸本)081946080X

By means of Artificial Neural Network and back-propagation algorithm, the multi-component of azo-dyes can be qualitatively and quantitatively analyzed simultaneously, though their Raman spectra arc overlapped. This article designed a back-propagation algorithm network to analyze the multi-component of azo-dyes (Sudan I and Sudan III). In conclusion, by using the Artificial Neural Network and Raman spectrum can be a good choice for resolving multi-component.

关键词： Artificial Neural Network back-propagation algorithm Raman spectrum multi-component analysis azo-dyes Sudan 1 Sudan III

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Research on BP Neural Network Optimal Method Based on Improved Ant Colony algorithm

Research on BP Neural Network Optimal Method Based on Improv...

引用

2nd International Conference on Computer Engineering and Applications

作者： Wang, Li Wang, Dong-qing Ding, Ning Univ Sci & Technol Beijing Sch Informat Engn Minist Educ Key Lab Adv Control Iron & Steel Proc Beijing 100083 Peoples R China

ISBN: (纸本)9780769539829

A method based on ant colony algorithm (ACA) is proposed to train weights and thresholds for back-propagation (BP) neural network. BP algorithm has been widely used in training artificial neural network (ANN). This algorithm has many attractive features, such as adaptive learning, self-organism, and fault tolerant ability. All of them make BP one of the most successful algorithms in various fields. But, BP suffers from relatively slow convergence speed, extensive computations and possible divergence for certain conditions. As a new bionic algorithm, ACA has gained very good performance in solving traveling salesman problem (TSP) and other optimization problems. Its properties such as distributed computation, heuristic searching and robustness have well conquered the long convergence speed and premature problem, which are the main deficiencies of BP algorithm. Experiments suggest the method proposed has resolved those problems efficiently.

关键词： improved Ant colony algorithm back-propagation algorithm artificial neural network

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Fault Diagnosis of Chemical Process Based on ACO-BP Neural Network

Fault Diagnosis of Chemical Process Based on ACO-BP Neural N...

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2nd International Conference on Advanced Design and Manufacturing Engineering (ADME 2012)

作者： Chen Jian-xue Shanghai Univ Engn Sci Coll Elect & Elect Engn Shanghai 201620 Peoples R China

ISBN: (纸本)9783037855027

Fault diagnosis is an important problem in the process of chemical industry and the artificial neural network is widely applied in fault diagnosis of chemical process. A hybrid algorithm combining ant colony optimization (ACO) algorithm with back-propagation (BP) algorithm, also referred to as ACO-BP algorithm, is proposed to train the neural network weights and thresholds. The basic theory and steps of ACO-BP algorithm are given, and applied in fault diagnosis of the continuous stirred-tank reactor (CSTR). Experimental results prove that ACO-BP algorithm has good fault diagnosis precision, and it can detect the fault in CSTR promptly and effectively.

关键词： Fault diagnosis Ant colony optimization algorithm back-propagation algorithm Neural network CSTR

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Pile samples selection method based on self-organizing maps neural network

Pile samples selection method based on self-organizing maps ...

引用

International Conference on Civil, Architectural and Hydraulic Engineering (ICCAHE 2012)

作者： Liu, Sisi Fan, Yunlei Cent South Univ Forestry & Technol Coll Civil Engn & Mech Changsha 410004 Hunan Peoples R China

ISBN: (纸本)9783037854846

One-dimensional self-organizing maps neural network (SOM) is used in pile samples selection, and the outcome can improve the accuracy of back-propagation neural network (BP) is proved. Firstly, 71 pile samples are divided into 5 groups according to SOM node weights. Each group is divided into training set, testing set, validation set to build 5 independent BP networks, called BP2. Secondly, 5 groups training set are merged into a new training set, similarly, a new test set and validation set to build another BP network, called BP1. At last, comparison of the performance of BP1 and BP2 show that using SOM network to select pile samples can build a BP network with better performance.

关键词： Pile Foundation Sample Selection Neural Network Self-Organizing Maps back-propagation algorithm

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3-Dimensional Path Planning for Autonomous Underwater Vehicle

3-Dimensional Path Planning for Autonomous Underwater Vehicl...

引用

Conference on OCEANS MTS/IEEE Charleston

作者： Wang, Yiheng Zheng, Yahong Rosa Missouri Univ Sci & Technol Dept ECE Rolla MO 65409 USA Lehigh Univ Dept ECE Bethlehem PA 18015 USA

ISBN: (纸本)9781538648148

This paper proposes a new algorithm for Autonomous Underwater Vehcle (AUV) path planning in 3D space to visit multiple targets using Dubins curves. For a given target-sequence, the 3D path planning is usually solved by two steps: Step 1 projects 3D targets to the X-Y plane and designs a 2D path on this plane;Step 2 maps the 2D path into 3D via interpolation. The proposed new algorithm defines a local coordinate system (LCS) for each pair of targets and designs the 2D Dubins curve in the LCS, then uses the Euler's rotation transformation to convert the 2D dubins curve into the 3D global coordinate system (GCS). Applying the proposed rotation method to a given target sequence and given incoming-outgoing angles yields 3D Dubins path with guaranteed G2 continuity at the joints of two Dubins curves. The proposed method is compared with the interpolation method and Bezier curve method. Computer simulations demonstrate that the proposed algorithm provides better G(2) continuity in 3D space or shorter path lengths than the existing linear or spline interpolation methods and Bezier curves.

关键词： Autonomous Underwater Vehicles AUV Path Planning 3D Dubins Curve Bezier Curve Geometric Continuity back-propagation algorithm

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Hybrid data analysis methods and Artificial Neural Network design in breast cancer diagnosis: IDEST experience

Hybrid data analysis methods and Artificial Neural Network d...

引用

International Conference on Computational Intelligence for Modelling, Control and Automation/International Conference on Intelligent Agents Web Technologies and International Commerce

作者： Bevilacqua, V. Mastronardi, G. Menolascina, F. DEE Polytech Bari Via Orabona 4 I-70125 Bari Italy

ISBN: (纸本)0769525040

This paper presents a method for breast cancer diagnosis using a system based on an Artificial Neural Network (ANN) trained using a particular version of the back propagation (BP) algorithm [1]. The Wisconsin Breast Cancer Database (WBCD)[2][3][4] was used in order to train and validate the ANN;WBCD is composed by 699 cases monitored by Doc. William H. Wolberg in the first 190s. The development of this system required an articulate phase of data analysis and preprocessing: various statistical tools like Principal Component Analysis (PCA) and Principal Factor Analysis (PFA), were used in order to find parameters more strictly correlated to the malignant/benignant nature of the cancer. Non linear data analysis techniques were employed to gain more knowledge about the internal structure of the database. A genetic algorithm was then set up to find the best topology of ANN. The analysis of results obtained by IDEST followed training and validation of the ANN.

关键词： Artificial Neural Networks back-propagation algorithm genetic algorithm breast cancer diagnosis medical diagnostic statistical analysis principal component analysis

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Modeling of Metal Inert Gas Welding Process Using Radial Basis Function Neural Networks

Modeling of Metal Inert Gas Welding Process Using Radial Bas...

引用

World Congress on Nature and Biologically Inspired Computing

作者： Datta, Somak Pratihar, D. K. Indian Inst Technol Dept Mech Engn Soft Comp Lab Kharagpur 721302 W Bengal India

ISBN: (纸本)9781424450534

In the present study, input-output relationships of metal inert gas welding process have modeled using radial basis function neural networks. As the performance of a neural network depends on its structure and parameters, some approaches have been developed to optimize them simultaneously. The performances of the developed approaches have been compared among them on some test cases. It has been observed that clustering plays an important role in deciding a suitable structure of the network. Moreover, it has been felt that a combined optimization scheme involving one global optimizer (a genetic algorithm) and one local optimizer (back-propagation algorithm) could be efficient to optimize both the structure and parameters of a network simultaneously.

关键词： Metal Inert Gas welding Radial Basis Function Neural Networks Genetic algorithm back-propagation algorithm Clustering

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Knowledge Based Approach for Diagnosis of Breast Cancer

Knowledge Based Approach for Diagnosis of Breast Cancer

引用

IEEE International Advance Computing Conference

作者： Shukla, Anupam Tiwari, Ritu Kaur, Prabhdeep ABV Indian Inst Informat Technol & Management Gwa Dept Informat Commun & Technol Gwalior India

ISBN: (纸本)9781424429271

This paper presents a novel approach to simulate a Knowledge Based System for diagnosis of Breast Cancer using Soft Computing tools like Artificial Neural Networks (ANNs) and Neuro Fuzzy Systems. The feed-forward neural network has been trained using three ANN algorithms, the back propagation algorithm (BPA), the Radial Basis Function (RBF) Networks and the Learning Vector Quantization (LVQ) Networks;and also by Adaptive Neuro Fuzzy Inference System (ANFIS). The simulator has been developed using MATLAB and performance is compared by considering the metrics like accuracy of diagnosis, training time, number of neurons, number of epochs etc. The simulation results show that this Knowledge Based Approach can be effectively used for early detection of Breast Cancer to help oncologists to enhance the survival rates significantly.

关键词： Knowledge Based System Breast cancer Diagnosis Neural Networks Neuro-Fuzzy Systems back-propagation algorithm Radial Basis Function Networks Learning Vector Quantization Networks Comparison

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