Soft Sensor Modeling Based on Radial Basis Function Neural Network and Fuzzy C-Means

2011 ◽  
Vol 219-220 ◽  
pp. 1263-1266
Author(s):  
Xi Huai Wang ◽  
Jian Mei Xiao
Keyword(s):  
Neural Network ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Soft Sensor ◽  
Fuzzy Cluster ◽  
Training Data ◽  
Temperature Estimation ◽  
Data Set ◽  
Slab Temperature ◽  
Walking Beam Reheating Furnace

A neural network soft sensor based on fuzzy clustering is presented. The training data set is separated into several clusters with different centers, the number of fuzzy cluster is decided automatically, and the clustering centers are modified using an adaptive fuzzy clustering algorithm in the online stage. The proposed approach has been applied to the slab temperature estimation in a practical walking beam reheating furnace. Simulation results show that the approach is effective.

scholarly journals A neural network model for estimation soil temperature bases on limited meteorological parameters in selected provinces in Iraq

2017 ◽  
Vol 2 (5) ◽  
Author(s):  
Ali M. Al-Salihi ◽  
Zahraa A. AL-Ramahy
Keyword(s):  
Neural Network ◽  
Soil Temperature ◽  
Hydrological Cycle ◽  
Absolute Error ◽  
Training Data ◽  
Percentage Error ◽  
Ann Model ◽  
Temperature Estimation ◽  
Data Set ◽  
Daily Data

Soil temperature is an important meteorological variable which plays a significant role in hydrological cycle. In present study, artificial intelligence technique employed for estimating for 3 daysa head soil temperature estimation at 10 and 20 cm depth. Soil temperature daily data for the period 1 January 2012 to 31 December 2013 measured in three stations namely (Mosul, Baghdad and Muthanna) in Iraq. The training data set includes 616 days and the testing data includes 109 days. The Levenberg-Marquardt, Scaled Conjugate Gradient and Bayesian regularization algorithms. To evaluate the ANN models, Root mean square error (RMSE), Mean absolute error (MAE), Mean absolute percentage error (MAPE) and Correlation Coefficient (r) were determined. According to the four statistical indices were calculated of the optimum ANN model, it was ANN model (3) in Muthanaa station for the depth 10 cm and ANN model (3) in Baghdad station for the depth 20 were (RMSE=0.959oC, MAE=0.725, MAPE=4.293, R=0.988) and (RMSE=0.887OC, MAE=0.704, MAPE=4.239, R=0.993) respectively, theses statistical criteria shown the efficiency of artificial neural network for soil temperature estimation.

scholarly journals Improved Extension Neural Network and Its Applications

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Yu Zhou ◽  
Lian Tian ◽  
Linfei Liu
Keyword(s):  
Neural Network ◽  
Supervised Learning ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Training Data ◽  
Learning Performance ◽  
Data Selection ◽  
Data Set ◽  
Application Range ◽  
Shadowed Sets

Extension neural network (ENN) is a new neural network that is a combination of extension theory and artificial neural network (ANN). The learning algorithm of ENN is based on supervised learning algorithm. One of important issues in the field of classification and recognition of ENN is how to achieve the best possible classifier with a small number of labeled training data. Training data selection is an effective approach to solve this issue. In this work, in order to improve the supervised learning performance and expand the engineering application range of ENN, we use a novel data selection method based on shadowed sets to refine the training data set of ENN. Firstly, we use clustering algorithm to label the data and induce shadowed sets. Then, in the framework of shadowed sets, the samples located around each cluster centers (core data) and the borders between clusters (boundary data) are selected as training data. Lastly, we use selected data to train ENN. Compared with traditional ENN, the proposed improved ENN (IENN) has a better performance. Moreover, IENN is independent of the supervised learning algorithms and initial labeled data. Experimental results verify the effectiveness and applicability of our proposed work.

Crack-Depth Determination by a Neural Network with a Synthetic Training Data Set

1993 ◽  
pp. 803-810 ◽  
Author(s):  
M. Takadoya ◽  
M. Notake ◽  
M. Kitahara ◽  
J. D. Achenbach ◽  
Q. C. Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Crack Depth ◽  
Training Data ◽  
Data Set ◽  
Depth Determination ◽  
Synthetic Training Data

scholarly journals Bootstrapping a Neural Morphological Generator from Morphological Analyzer Output for Inuktitut

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Jeffrey Micher
Keyword(s):  
Neural Network ◽  
Training Data ◽  
Data Set ◽  
Set Size ◽  
The Neural Network ◽  
Surface Character ◽  
Finite State ◽  
Character Sequences ◽  
Finite State Transducer

We present a method for building a morphological generator from the output of an existing analyzer for Inuktitut, in the absence of a two-way finite state transducer which would normally provide this functionality. We make use of a sequence to sequence neural network which “translates” underlying Inuktitut morpheme sequences into surface character sequences. The neural network uses only the previous and the following morphemes as context. We report a morpheme accuracy of approximately 86%. We are able to increase this accuracy slightly by passing deep morphemes directly to output for unknown morphemes. We do not see significant improvement when increasing training data set size, and postulate possible causes for this.

The Application of Fuzzy Clustering Number Algorithm in Network Intrusion Detection

2013 ◽  
Vol 760-762 ◽  
pp. 2220-2223
Author(s):  
Lang Guo
Keyword(s):  
Intrusion Detection ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Local Optimum ◽  
Cluster Number ◽  
Data Set ◽  
Network Intrusion ◽  
Correlation Degree ◽  
Indicator Data ◽  
Detection Effect

In view of the defects of K-means algorithm in intrusion detection: the need of preassign cluster number and sensitive initial center and easy to fall into local optimum, this paper puts forward a fuzzy clustering algorithm. The fuzzy rules are utilized to express the invasion features, and standardized matrix is adopted to further process so as to reflect the approximation degree or correlation degree between the invasion indicator data and establish a similarity matrix. The simulation results of KDD CUP1999 data set show that the algorithm has better intrusion detection effect and can effectively detect the network intrusion data.

scholarly journals Modeling of daily runoff from a small agricultural watershed using artificial neural network with resampling techniques

2014 ◽  
Vol 17 (1) ◽  
pp. 56-74 ◽  
Author(s):  
Gurjeet Singh ◽  
Rabindra K. Panda ◽  
Marc Lamers
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Agricultural Watershed ◽  
Training Data ◽  
Optimum Number ◽  
Training Process ◽  
Data Set ◽  
Reported Study ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The reported study was undertaken in a small agricultural watershed, namely, Kapgari in Eastern India having a drainage area of 973 ha. The watershed was subdivided into three sub-watersheds on the basis of drainage network and land topography. An attempt was made to relate the continuously monitored runoff data from the sub-watersheds and the whole-watershed with the rainfall and temperature data using the artificial neural network (ANN) technique. The reported study also evaluated the bias in the prediction of daily runoff with shorter length of training data set using different resampling techniques with the ANN modeling. A 10-fold cross-validation (CV) technique was used to find the optimum number of hidden neurons in the hidden layer and to avoid neural network over-fitting during the training process for shorter length of data. The results illustrated that the ANN models developed with shorter length of training data set avoid neural network over-fitting during the training process, using a 10-fold CV method. Moreover, the biasness was investigated using the bootstrap resampling technique based ANN (BANN) for short length of training data set. In comparison with the 10-fold CV technique, the BANN is more efficient in solving the problems of the over-fitting and under-fitting during training of models for shorter length of data set.

A Fuzzy Cluster Algorithm of Heavy Metal City Topsoil Pollution

2013 ◽  
Vol 830 ◽  
pp. 341-344
Author(s):  
Jun Jun Du ◽  
Sheng Ping Jin ◽  
Qiong Li ◽  
She Sheng Zhang
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Fuzzy Ahp ◽  
Science Research ◽  
Fuzzy Cluster ◽  
Metal Pollutants ◽  
Fuzzy Cluster Algorithm ◽  
Heavy Metal Pollutants

Consider heavy metal pollution of topsoil in the city of world today is a hot science research project. A fuzzy clustering algorithm l is constructed ed by analyzing the propagation characteristics of heavy metal pollutants. Considering topography, areas, factories, roads, , irredentist, etc. we calculate a evaluation on comprehensive pollution, and the degree of heavy metals pollution, by using fuzzy clustering and fuzzy AHP. The results show that the index of the comprehensive pollution of heavy metals on the region, and the weight of pollution of each category.

The Application of Probabilistic Neural Network in Speech Recognition Based on Partition Clustering

2012 ◽  
Vol 263-266 ◽  
pp. 2173-2178
Author(s):  
Xin Guang Li ◽  
Min Feng Yao ◽  
Li Rui Jian ◽  
Zhen Jiang Li
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Clustering Algorithm ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Partition Clustering

A probabilistic neural network (PNN) speech recognition model based on the partition clustering algorithm is proposed in this paper. The most important advantage of PNN is that training is easy and instantaneous. Therefore, PNN is capable of dealing with real time speech recognition. Besides, in order to increase the performance of PNN, the selection of data set is one of the most important issues. In this paper, using the partition clustering algorithm to select data is proposed. The proposed model is tested on two data sets from the field of spoken Arabic numbers, with promising results. The performance of the proposed model is compared to single back propagation neural network and integrated back propagation neural network. The final comparison result shows that the proposed model performs better than the other two neural networks, and has an accuracy rate of 92.41%.

Helicopter Motion Control Using a General Regression Neural Network

2011 ◽  
pp. 41-68 ◽  
Author(s):  
T. G.B. Amaral ◽  
M. M. Crisostomo ◽  
V. Fernao Pires
Keyword(s):  
Neural Network ◽  
Clustering Algorithm ◽  
Training Data ◽  
General Regression Neural Network ◽  
Parallel Structure ◽  
Data Sets ◽  
Learning Capability ◽  
Fast Learning ◽  
Control Flight ◽  
General Regression

This chapter describes the application of a general regression neural network (GRNN) to control the flight of a helicopter. This GRNN is an adaptive network that provides estimates of continuous variables and is a one-pass learning algorithm with a highly parallel structure. Even with sparse data in a multidimensional measurement space, the algorithm provides smooth transitions from one observed value to another. An important reason for using the GRNN as a controller is the fast learning capability and its non-iterative process. The disadvantage of this neural network is the amount of computation required to produce an estimate, which can become large if many training instances are gathered. To overcome this problem, it is described as a clustering algorithm to produce representative exemplars from a group of training instances that are close to one another reducing the computation amount to obtain an estimate. The reduction of training data used by the GRNN can make it possible to separate the obtained representative exemplars, for example, in two data sets for the coarse and fine control. Experiments are performed to determine the degradation of the performance of the clustering algorithm with less training data. In the control flight system, data training is also reduced to obtain faster controllers, maintaining the desired performance.

scholarly journals Fast Linear Adaptive Skipping Training Algorithm for Training Artificial Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
R. Manjula Devi ◽  
S. Kuppuswami ◽  
R. C. Suganthe
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Training Model ◽  
Training Data ◽  
Experimental Result ◽  
Training Algorithms ◽  
Data Set ◽  
Training Time ◽  
Input Sample ◽  
Artificial Neural

Artificial neural network has been extensively consumed training model for solving pattern recognition tasks. However, training a very huge training data set using complex neural network necessitates excessively high training time. In this correspondence, a new fast Linear Adaptive Skipping Training (LAST) algorithm for training artificial neural network (ANN) is instituted. The core essence of this paper is to ameliorate the training speed of ANN by exhibiting only the input samples that do not categorize perfectly in the previous epoch which dynamically reducing the number of input samples exhibited to the network at every single epoch without affecting the network’s accuracy. Thus decreasing the size of the training set can reduce the training time, thereby ameliorating the training speed. This LAST algorithm also determines how many epochs the particular input sample has to skip depending upon the successful classification of that input sample. This LAST algorithm can be incorporated into any supervised training algorithms. Experimental result shows that the training speed attained by LAST algorithm is preferably higher than that of other conventional training algorithms.

Sign in / Sign up

Export Citation Format

Share Document