Numerical Comparison of Shapeless Radial Basis Function Networks in Pattern Recognition

This work focuses on radial basis functions containing no parameters with themain objective being to comparatively explore more of their effectiveness.For this,a total of sixteen forms of shapeless radial basis functions are gathered and investigated under the context of the pattern recognition problem through the structure of radial basis function neural networks,with the use of the Representational Capability(RC)algorithm.Different sizes of datasets are disturbed with noise before being imported into the algorithm as‘training/testing’datasets.Each shapeless radial basis function is monitored carefully with effectiveness criteria including accuracy,condition number(of the interpolation matrix),CPU time,CPU-storage requirement,underfitting and overfitting aspects,and the number of centres being generated.For the sake of comparison,the well-known Multiquadric-radial basis function is included as a representative of shape-contained radial basis functions.The numerical results have revealed that some forms of shapeless radial basis functions show good potential and are even better than Multiquadric itself indicating strongly that the future use of radial basis function may no longer face the pain of choosing a proper shape when shapeless forms may be equally(or even better)effective.

Crack Fault Diagnosis and Location Method for a Dual-Disk Hollow Shaft Rotor System Based on the Radial Basis Function Network and Pattern Recognition Neural Network

The crack fault is one of the most common faults in the rotor system,and researchers have paid close attention to its fault diagnosis.However,most studies focus on discussing the dynamic response characteristics caused by the crack rather than estimating the crack depth and position based on the obtained vibration signals.In this paper,a novel crack fault diagnosis and location method for a dual-disk hollow shaft rotor system based on the Radial basis function(RBF)network and Pattern recognition neural network(PRNN)is presented.Firstly,a rotor system model with a breathing crack suitable for a short-thick hollow shaft rotor is established based on the finite element method,where the crack's periodic opening and closing pattern and different degrees of crack depth are considered.Then,the dynamic response is obtained by the harmonic balance method.By adjusting the crack parameters,the dynamic characteristics related to the crack depth and position are analyzed through the amplitude-frequency responses and waterfall plots.The analysis results show that the first critical speed,first subcritical speed,first critical speed amplitude,and super-harmonic resonance peak at the first subcritical speed can be utilized for the crack fault diagnosis.Based on this,the RBF network and PRNN are adopted to determine the depth and approximate location of the crack respectively by taking the above dynamic characteristics as input.Test results show that the proposed method has high fault diagnosis accuracy.This research proposes a crack detection method adequate for the hollow shaft rotor system,where the crack depth and position are both unknown.

Pattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments

In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occurred＇ and transfer ＇not occurred＇. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation （multilayer perceptron algorithms） could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties （e.g., temperature, density） show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.

Prediction of Enthalpies of Fusion for Divalent Rare Earth Halides Based on Modeling by Artificial Neural Networks and Pattern Recognition

The artificial neural network (ANN) and the pattern recognition were applied to study the correlation of enthalpies of fusion for divalent rare earth halides with their microstructural parameters,such as ionic radius and electronegativity. The model,represented by a back-propagation netal network, was trained with a 12 set of published data for divalent rare earth halides and then was used to predict the unknown ones. Also the criterion equations were ptesented to determine the enthalpies of fuSion for divalent rare earth halides using pattern recognition in mis work. The results from the model in ANN and criterion equations are in very good agreement with reference data.

Sensor Network Structure Recognition Based on P-law

A sensor graph network is a sensor network model organized according to graph network structure.Structural unit and signal propagation of core nodes are the basic characteristics of sensor graph networks.In sensor networks,network structure recognition is the basis for accurate identification and effective prediction and control of node states.Aiming at the problems of difficult global structure identification and poor interpretability in complex sensor graph networks,based on the characteristics of sensor networks,a method is proposed to firstly unitize the graph network structure and then expand the unit based on the signal transmission path of the core node.This method which builds on unit patulousness and core node signal propagation(called p-law)can rapidly and effectively achieve the global structure identification of a sensor graph network.Different from the traditional graph network structure recognition algorithms such as modularity maximization and spectral clustering,the proposed method reveals the natural evolution process and law of graph network subgroup generation.Experimental results confirm the effectiveness,accuracy and rationality of the proposed method and suggest that our method can be a new approach for graph network global structure recognition.

Application of extension neural network to safety status pattern recognition of coalmines

In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of network,the rationale of recognition algorithm and the performance of proposed method were discussed in detail.The safety status pattern recognition problem of coalmines can be regard as a classification problem whose features are defined in a range,so using the ENN is most appropriate for this problem.The ENN-based recognition method can use a novel extension distance to measure the similarity between the object to be recognized and the class centers.To demonstrate the effectiveness of the proposed method,a real-world application on the geological safety status pattern recognition of coalmines was tested.Comparative experiments with existing method and other traditional ANN-based methods were conducted.The experimental results show that the proposed ENN-based recognition method can identify the safety status pattern of coalmines accurately with shorter learning time and simpler structure.The experimental results also confirm that the proposed method has a better performance in recognition accuracy,generalization ability and fault-tolerant ability,which are very useful in recognizing the safety status pattern in the process of coal production.

Pattern recognition and prediction study of rock burst based on neural network

Many monitoring measures were used in the production field for predicting rockburst.However, predicting rock burst according to complicated observation data is alwaysa pressing problem in this research field.Though the critical value method gets extensiveapplication in practice, it stresses only on the superficial change of data and overlooks alot of features of rock burst and useful information that is concealed and hidden in the observationtime series.Pattern recognition extracts the feature value of time domain, frequencydomain and wavelet domain in observation time series to form Multi-Feature vectors,using Euclidean distance measure as the separable criterion between the same typeand different type to compress and transform feature vectors.It applies neural network asa tool to recognize the danger of rock burst, and uses feature vectors being compressedto carry out training and studying.It is proved by test samples that predicting precisionshould be prior to such traditional predicting methods as pattern recognition and critical indicatormethod.

Fingerprint Recognition with Artificial Neural Networks: Application to E-Learning

Fingerprint recognition is a mature biometric technique for identification or authentication application. In this work, we describe a method based on the use of neural network to authenticate people who want to accede to an automated fingerprint system for E-learning. The idea is to apply back propagation algorithm on a multilayer perceptron during the training stage. One of the advantages of this technique is the use of a hidden layer which allows the network to make comparison by calculating probabilities on template which are invariant to translation and rotation. Results come both from the NIST special database 4 and a local database, and show that a proposed method gives good results in some cases.

Visualization of flatness pattern recognition based on T-S cloud inference network

Flatness pattern recognition is the key of the flatness control. The accuracy of the present flatness pattern recognition is limited and the shape defects cannot be reflected intuitively. In order to improve it, a novel method via T-S cloud inference network optimized by genetic algorithm(GA) is proposed. T-S cloud inference network is constructed with T-S fuzzy neural network and the cloud model. So, the rapid of fuzzy logic and the uncertainty of cloud model for processing data are both taken into account. What's more, GA possesses good parallel design structure and global optimization characteristics. Compared with the simulation recognition results of traditional BP Algorithm, GA is more accurate and effective. Moreover, virtual reality technology is introduced into the field of shape control by Lab VIEW, MATLAB mixed programming. And virtual flatness pattern recognition interface is designed.Therefore, the data of engineering analysis and the actual model are combined with each other, and the shape defects could be seen more lively and intuitively.

A Fuzzy Neural Network for Fault Pattern Recognition

This paper combines fuzzy set theory with ART neural net-work , and demonstrates some important properties of the fuzzy ART neural net-work algorithm. The results from application on a ball bearing diagnosis indicate that a fuzzy ART neural net-work has an effect of fast stable recognition for fuzzy patterns.

Prediction of Composition of GaInAsSb Epilayers by MOCVD Using Pattern Recognition and Artificial Neural Network Method

he pattern recognition method and artificial neural network method to predict the composition of epilayer of GaInAsSb by MOCVD. It is concluded that a neural network with the composition of the vapor phase and growth temperature as training data can predict the composition of the epilayers. Satisfactory pattern recognition and artificial neural network classification results were obtained by using four technical parameters as characteristic features and the epilayers composition as classification criteria.

MOLTEN SALT PHASE DIAGRAMS CALCULATION USING ARTIFICIAL NEURAL NETWORK OR PATTERN RECOGNITION-BOND PARAMETERS

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A CASCADED MODEL OF NEURAL NETWORK FOR PATTERN RECOGNITION

A cascaded model of neural network and its learning algorithm suitable for opticalimplementation are proposed.Computer simulations have shown that this model may successfullybe applied to an error-tolerance pattern recognitions of multiple 3-D targets with arbitrary spatialorientations.

Model of Computer Network Topology Optimization Based on Pattern Recognition Technology

The topological structure of the computer network has an important influence on the efficiency of the whole network system,the exertion of the technical performance, the reliability and the cost. The concept of computer network topology classification, introduces thecharacteristics, based on the analysis of the complex network structure, explore the effective construction of computer network topology model,for its practical application in the design of redundancy is studied, to improve the network system reliability and system safety design，

Influence of Blurred Ways on Pattern Recognition of a Scale-Free Hopfield Neural Network

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2D spiral pattern recognition based on neural network covering algorithm

The main aim for a 2D spiral recognition algorithm is to learn to discriminate between data distributed on two distinct strands in the x-y plane.This problem is of critical importance since it incorporates temporal characteristics often found in real-time applications.Previous work with this benchmark has witnessed poor results with statistical methods such as discriminant analysis and tedious procedures for better results with neural networks.This paper presents a max-density covering learning algorithm based on constructive neural networks which is efficient in terms of the recognition rate and the speed of recognition.The results show that it is possible to solve the spiral problem instantaneously(up to 100% correct classification on the test set).

MOVING TARGETS PATTERN RECOGNITION BASED ON THE WAVELET NEURAL NETWORK

Based on pattern recognition theory and neural network technology, moving objects automatic detection and classification method integrating advanced wavelet analysis are discussed in detail. An algorithm of moving targets pattern recognition on the combination of inter-frame difference and wavelet neural network is presented. The experimental results indicate that the designed BP wavelet network using this algorithm can recognize and classify moving targets rapidly and effectively.

Recognition system of leaf images based on neuronal network

In forest variety registration, visual traits of the plants appearance are widely used to discern different tree species. The new recognition system of leaf image strategy which based on neural network established to administrate a hierarchical list of leaf images, some sorts of edge detection can be performed to identify the individual tokens of every image and the frame of the leaf can be got to differentiate the tree species. An approach based on back-propagation neuronal network is proposed and the programming language for the implementation is also Riven by using Java. The numerical simulations results have shown that the proposed leaf strategt is effective and feasible.