Radial Basis Probabilistic Neural Network Research Articles

A deep image classification model based on prior feature knowledge embedding and application in medical diagnosis

Aiming at the problem of image classification with insignificant morphological structural features, strong target correlation, and low signal-to-noise ratio, combined with prior feature knowledge embedding, a deep learning method based on ResNet and Radial Basis Probabilistic Neural Network (RBPNN) is proposed model. Taking ResNet50 as a visual modeling network, it uses feature pyramid and self-attention mechanism to extract appearance and semantic features of images at multiple scales, and associate and enhance local and global features. Taking into account the diversity of category features, channel cosine similarity attention and dynamic C-means clustering algorithms are used to select representative sample features in different category of sample subsets to implicitly express prior category feature knowledge, and use them as the kernel centers of radial basis probability neurons (RBPN) to realize the embedding of diverse prior feature knowledge. In the RBPNN pattern aggregation layer, the outputs of RBPN are selectively summed according to the category of the kernel center, that is, the subcategory features are combined into category features, and finally the image classification is implemented based on Softmax. The functional module of the proposed method is designed specifically for image characteristics, which can highlight the significance of local and structural features of the image, form a non-convex decision-making area, and reduce the requirements for the completeness of the sample set. Applying the proposed method to medical image classification, experiments were conducted based on the brain tumor MRI image classification public dataset and the actual cardiac ultrasound image dataset, and the accuracy rate reached 85.82% and 83.92% respectively. Compared with the three mainstream image classification models, the performance indicators of this method have been significantly improved.

Microseismic strength prediction based on radial basis probabilistic neural network

ABSTRACT This paper comprehensively adopts acoustic emission monitoring signals, ground stress monitoring signals, mining production data, energy evolution process data, microseismic statistics, mine geological structure and multidisciplinary data in subjects such as engineering mechanics, as well as existing cognitive laws to study and establish radial basis function neural network model for time-varying process signal analysis. Based on focal region localization and time-space environment correction alignment, the study bases itself on probability statistics theory and big data analysis technology. It studies sample process characteristics and the governing law in the already microseismic and periodic weighting events to investigate the statistical laws, trends and critical characteristics behind the event sample data so that microseismic magnitude and risk degree can be predicted. By changing the parameters of the nonlinear transformation function of the Neuron to realize the nonlinear mapping and the linearization of the connection weight adjustment, the learning speed of the network is improved. Compared with other dynamic neural network models which can deal with time-varying signal classification, the computational complexity is greatly reduced.

A radial basis probabilistic process neural network model and corresponding classification algorithm

A radial basis probabilistic process neuron (RBPPN) and radial basis probabilistic process neural network (RBPPNN) model are proposed to fuse a priori knowledge for application to time-varying signal pattern classification. RBPPN inputs were multi-channel time-varying signals and a generalized inner product was used to perform spatio-temporal aggregation of input signals in the kernel. Typical signal samples from various pattern subsets in the sample set were used as kernel center functions, which use morphological distribution characteristics and combination relationships to implicitly express prior knowledge for the signal category. The exponential probability function was used as the activation function to achieve kernel transformation and RBPPN probability output. The RBPPNN is composed of process signal input layers, an RBPPN hidden layer, a pattern layer, and a Softmax classifier developed through stacking. Generalized inner product operations were used to conduct probability similarity measurements of distribution characteristics between process signals. The pattern layer selectivity summed inputs from the RBPPN hidden layer to the pattern layer according to the category of the kernel center function. Its outputs were then used as inputs in the Softmax classifier. The proposed RBPPNN information processing mechanism was extended to the time domain, and through learning time-varying signal training samples, achieved extraction, expression, and information association of time-varying signal characteristics, as well as direct classification. It can improve the deficiencies of existing neural networks, such as a complete large-scale training dataset is needed, and the information processing flow is complex. In this paper, the properties of the RBPPNN are analyzed and a specific learning algorithm is presented which synthesizes dynamic time warping, dynamic C-means clustering, and the mean square error algorithm. A series of 12-lead electrocardiogram (ECG) signals were used for classification testing of heart disease diagnosis results. The ECG classification accuracy across ten disease types was 75.52% and sinus arrhythmia was identified with an accuracy of 86.75%, verifying the effectiveness of the model and algorithm.

Detection of Cognitive and Visual Distraction Using Radial Basis Probabilistic Neural Networks

This paper suggests a real-time method for detecting a driver’s cognitive and visual distraction using lateral driving performance measures. The algorithm adopts radial basis probabilistic neural networks (RBPNNs) to construct classification models. In this study, combinations of two driving performance data measures, including the standard deviation of lane position (SDLP) and steering wheel reversal rate (SRR), were considered as measures of distraction. Data for training and testing the RBPNN models were collected under simulated conditions in which fifteen participants drove on a highway. While driving, they were asked to complete auditory recall tasks or arrow search tasks to create cognitively or visually distracted driving periods. As a result, the best performing model could detect distraction with an average accuracy of 78.0 %, which is a relatively high accuracy in the human factors domain. The results demonstrated that the RBPNN model using SDLP and SRR could be an effective distraction detector with easy-to-obtain and inexpensive inputs.

A novel training method to preserve generalization of RBPNN classifiers applied to ECG signals diagnosis

In this paper a novel training technique is proposed to offer an efficient solution for neural network training in non-trivial and critical applications such as the diagnosis of health threatening illness. The presented technique aims to enhance the generalization capability of a neural network while preserving its sensitivity and precision. The implemented method has been devised in order to slowly increase, during training, the generalization capabilities of a Radial Basis Probabilistic Neural Network classifier, as well as preventing it from over-generalization and the consequent lack of resulting classification performances. The developed method was tested on Electrocardiograms. These latter are generally considered non-trivial both due to the difficulty to recognize some anomalous heart activities, and due to the intermittent nature of abnormal beat occurrences. The implemented training method obtained satisfactory performances, sensitivity and precision while showing high generalization capabilities.

Age-related alterations in the sign series entropy of short-term pulse rate variability

Pulse rate variability (PRV) has been certified as a substitute for heart rate variability (HRV) to diagnose and predict some diseases. Sign series entropy analysis (SSEA), a kind of nonlinear method, has been used to analyze HRV signal effectively. However, the time consumption of SSEA is too long, and it is unknown whether SSEA is suitable for analyzing short-term PRV signals. Therefore, an improved SSEA method named sliding window iterative SSEA (SWISSEA) is proposed to analyze short-term PRV signal and derive some age-related alterations. Moreover, a radial basis probabilistic neural network (RBPNN) based algorithm is proposed to classify subjects according to their ages. Continuous non-invasive blood pressure signals from the MIT-BIH database are chosen to generate short-term PRV signals as the experimental data, and their time domain and frequency domain parameters are extracted and selected for classifying. The experimental results show that the pulse beats are more uniform as the increase of the age, the sign series entropy (SSE) increases with aging and has a significant difference between young and old subjects even if the PRV signal is corrupted by heavy noises, the approach based on RBPNN can accurately classify subjects according their ages. In addition, the SWISSEA reduces the time consumption of SSEA, is more suitable for analyzing short-term PRV signals in real time, and has a potential in portable medical devices.

Classification of Penaeid Prawn Species using Radial basis Probabilistic Neural Networks and Support Vector Machines

This research is to present a new approach for the classification of the Penaeid Prawn Species. The extraction of Texture features based on the Gabor filter is proposed in this method. These extracted features are used for the classification of Penaeid Prawn Species based on Radial Basis Probabilistic Neural Networks and Support Vector Machines. The texture of the prawn image are extracted based on different scales and orientations by which mean and standard deviation are calculated. The resultant Gabor feature values are fed as input to Radial basis Probabilistic Neural Network Classifier for the classification of the species. The experimental results show the performance of the extracted feature vectors for Penaeid Prawn species recognition. The RBPNN gives better recognition when compared with Support vector machines.

MONITORING RUBBER FACTORY MALODOUR USING ARTIFICIAL NEURAL NETWORK

Malodour from rubber factory activities is a nuisance to the residence in the surrounding area. The volatile chemical compounds emitted from the factory are identified as the major source of the malodour. This research proposes a study of malodour monitoring for the rubber factory using Electronic Nose (e-nose) system to enable effective malodour monitoring. The proposed system will able to predict and monitor the malodour concentration in the surrounding area of the factory. The finding will be used as a guideline for monitoring the violations of environmental guidelines. The malodour and environment parameters such as temperature, wind speeds and directions are recorded. These parameters are measured on-site and monitored using wireless sensor network (WSN) system. The malodour concentration model of each location will generate using three types of Artificial Neural Network (ANN) i.e. Multilayer Perceptron (MLP), Radial Basis Function (RBF) and Probabilistic Neural Network (PNN). These indices will be used to generate the malodour concentration mapping model of the factory and surrounding areas. The initial results show that the system can be effectively used for real time rubber factory malodour monitoring.

Invariant face recognition using Zernike moments combined with feed forward neural network

The paper proposes a face recognition system using Zernike moments ZM and feed forward neural network as a classifier. Magnitudes of the ZM, which are invariant to rotation, are used as feature vectors for efficient representation of the images. The experiment was conducted on the ORL and Texas 3D Face Recognition Database which has both colour and range images. The recognition performance with measures like overall recognition accuracy, false acceptance rate, false rejection rate and true rejection rate was evaluated with multilayer perceptron neural network, radial basis function neural network and probabilistic neural network for variable lengths of the feature vector using confusion matrix. The simulation results indicates that the invariant ZM with neural network classifier was successful in recognising the images constrained to different variations and illumination conditions. The overall classification accuracy of 99.7% with MLPNN and 99.6% with MLPNN was achieved with range images and grey images from Texas 3D Face Recognition Database, respectively. Furthermore, 99.5% accuracy with RBFNN was achieved from ORL database.

Lithology identification based on genetic optimized radial basis probabilistic neural network

Lithology identification is the most critical procedure in the logging data interpretation field,while the traditional lithology identification methods have a lot of defects such as slow explain efficiency,low accuracy,and big influenced human factors.To resolve these problems,a new kind lithology identification method was put forward using genetic optimized Radial Basis Probability Neural Network(RBPNN).Probabilistic Neural Network(PNN) and the Radial Basis Function Neural Network(RBFNN) were combined to construct RBPNN.To optimize network structure,upgrade convergence speed and accuracy,Genetic Algorithm(GA) was used to search for the optimal hidden center vector and matching kernel function control parameters of the RBPNN structure which must satisfy minimum error of RBPNN training and form genetic optimized RBPNN network model.The case study shows that lithology identification based on genetic optimized RBPNN can achieve the actual application standards,and it is feasible and effective,it also can provide scientific theoretical supports and dependences for oil geological exploration field.

Diagnosis of Hashimoto’s thyroiditis in ultrasound using tissue characterization and pixel classification

Hashimoto's thyroiditis is the most common type of inflammation of the thyroid gland, and accurate diagnosis of Hashimoto's thyroiditis would be helpful to better manage the disease process and predict thyroid failure. Most of the published computer-based techniques that use ultrasound thyroid images for Hashimoto's thyroiditis diagnosis are limited by lack of procedure standardization because individual investigators use various initial ultrasound settings. This article presents a computer-aided diagnostic technique that uses grayscale features and classifiers to provide a more objective and reproducible classification of normal and Hashimoto's thyroiditis-affected cases. In this paradigm, we extracted grayscale features based on entropy, Gabor wavelet, moments, image texture, and higher order spectra from the 100 normal and 100 Hashimoto's thyroiditis-affected ultrasound thyroid images. Significant features were selected using t-test. The resulting feature vectors were used to build the following three classifiers using tenfold stratified cross validation technique: support vector machine, k-nearest neighbor, and radial basis probabilistic neural network. Our results show that a combination of 12 features coupled with support vector machine classifier with the polynomial kernel of order 1 and linear kernel gives the highest accuracy of 80%, sensitivity of 76%, specificity of 84%, and positive predictive value of 83.3% for the detection of Hashimoto's thyroiditis. The proposed computer-aided diagnostic system uses novel features that have not yet been explored for Hashimoto's thyroiditis diagnosis. Even though the accuracy is only 80%, the presented preliminary results are encouraging to warrant analysis of more such powerful features on larger databases.

AUTOMATED IDENTIFICATION OF EPILEPTIC AND ALCOHOLIC EEG SIGNALS USING RECURRENCE QUANTIFICATION ANALYSIS

Epilepsy is a common neurological disorder characterized by recurrence seizures. Alcoholism causes organic changes in the brain, resulting in seizure attacks similar to epileptic fits. Hence, it is challenging to differentiate the cause of fits as epileptic or alcoholism, which is important for deciding on the treatment in the neurology ward. The focus of this paper is to automatically differentiate epileptic, normal, and alcoholic electroencephalogram (EEG) signals. As the EEG signals are non-linear and dynamic in nature, it is difficult to tell the subtle changes in these signals with the help of linear techniques or by the naked eye. Therefore, to analyze the normal (control), epileptic, and alcoholic EEG signals, two non-linear methods, such as recurrence plots (RPs) and then recurrence quantification analysis (RQA) are adopted. Approximately 10 RQA parameters have been used to classify the EEG signals into three distinct classes, i.e., normal, epileptic, and alcoholic. Six classifiers, such as support vector machine (SVM), radial basis probabilistic neural network (RBPNN), decision tree (DT), Gaussian mixture model (GMM), k-nearest neighbor (kNN), and fuzzy Sugeno classifiers have been developed to accomplish this task. Results show that the GMM classifier outperformed the other classifiers with a classification sensitivity of 99.6%, specificity of 98.3%, and accuracy of 98.6%.

Atherosclerotic Risk Stratification Strategy for Carotid Arteries Using Texture-Based Features

Plaques in the carotid artery result in stenosis, which is one of the main causes for stroke. Patients have to be carefully selected for stenosis treatments as they carry some risk. Since patients with symptomatic plaques have greater risk for strokes, an objective classification technique that classifies the plaques into symptomatic and asymptomatic classes is needed. We present a computer aided diagnostic (CAD) based ultrasound characterization methodology (a class of Atheromatic systems) that classifies the patient into symptomatic and asymptomatic classes using two kinds of datasets: (1) plaque regions in ultrasound carotids segmented semi-automatically and (2) far wall gray-scale intima-media thickness (IMT) regions along the common carotid artery segmented automatically. For both kinds of datasets, the protocol consists of estimating texture-based features in frameworks of local binary patterns (LBP) and Law’s texture energy (LTE) and applying these features for obtaining the training parameters, which are then used for classification. Our database consists of 150 asymptomatic and 196 symptomatic plaque regions and 342 IMT wall regions. When using the Atheromatic-based system on semiautomatically determined plaque regions, support vector machine (SVM) classifier was adapted with highest accuracy of 83%. The accuracy registered was 89.5% on the far wall gray-scale IMT regions when using SVM, K-nearest neighbor (KNN) or radial basis probabilistic neural network (RBPNN) classifiers. LBP/LTE-based techniques on both kinds of carotid datasets are noninvasive, fast, objective and cost-effective for plaque characterization and, hence, will add more value to the existing carotid plaque diagnostics protocol. We have also proposed an index for each type of datasets: AtheromaticPi, for carotid plaque region, and AtheromaticWi, for IMT carotid wall region, based on the combination of the respective significant features. These indices show a separation between symptomatic and asymptomatic by 4.53 units and 4.42 units, respectively, thereby supporting the texture hypothesis classification.

Palmprint recognition method based on localized non-negative sparse coding

To more effectively extract localized features of images,on the basis of the traditional Non-negative Sparse Coding(Hoyer NNSC) algorithm,a novel localized NNSC(LNNSC) algorithm with sparse constraint was proposed.This algorithm considered the sparse measure constraint of feature basis vectors and the maximized representativeness of features,and could obtain the strengthened localized image features.At the same time,this algorithm utilized the Laplace density model as the feature coefficients sparse punitive function to ensure an image's sparse structure.Furthermore,on the basis of feature extraction,by utilizing the Radial Basis Probabilistic Neural Networks(RBPNN),the palmprint recognition task could be implemented automatically.Compared with the palmprint recognition methods of Non-negative Matrix Factorization(NMF),Local NMF(LNMF) and Hoyer-NNSC,simulation results show that our method proposed here displays feasibility and practicality in palmprint recognition.

APPLICATION OF NON-LINEAR AND WAVELET BASED FEATURES FOR THE AUTOMATED IDENTIFICATION OF EPILEPTIC EEG SIGNALS

Epilepsy, a neurological disorder, is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals, which are used to detect the presence of seizures, are non-linear and dynamic in nature. Visual inspection of the EEG signals for detection of normal, interictal, and ictal activities is a strenuous and time-consuming task due to the huge volumes of EEG segments that have to be studied. Therefore, non-linear methods are being widely used to study EEG signals for the automatic monitoring of epileptic activities. The aim of our work is to develop a Computer Aided Diagnostic (CAD) technique with minimal pre-processing steps that can classify all the three classes of EEG segments, namely normal, interictal, and ictal, using a small number of highly discriminating non-linear features in simple classifiers. To evaluate the technique, segments of normal, interictal, and ictal EEG segments (100 segments in each class) were used. Non-linear features based on the Higher Order Spectra (HOS), two entropies, namely the Approximation Entropy (ApEn) and the Sample Entropy (SampEn), and Fractal Dimension and Hurst Exponent were extracted from the segments. Significant features were selected using the ANOVA test. After evaluating the performance of six classifiers (Decision Tree, Fuzzy Sugeno Classifier, Gaussian Mixture Model, K-Nearest Neighbor, Support Vector Machine, and Radial Basis Probabilistic Neural Network) using a combination of the selected features, we found that using a set of all the selected six features in the Fuzzy classifier resulted in 99.7% classification accuracy. We have demonstrated that our technique is capable of achieving high accuracy using a small number of features that accurately capture the subtle differences in the three different types of EEG (normal, interictal, and ictal) segments. The technique can be easily written as a software application and used by medical professionals without any extensive training and cost. Such software can evolve into an automatic seizure monitoring application in the near future and can aid the doctors in providing better and timely care for the patients suffering from epilepsy.

Non-invasive automated 3D thyroid lesion classification in ultrasound: A class of ThyroScan™ systems

Ultrasound-based thyroid nodule characterization into benign and malignant types is limited by subjective interpretations. This paper presents a Computer Aided Diagnostic (CAD) technique that would present more objective and accurate classification and further would offer the physician a valuable second opinion. In this paradigm, we first extracted the features that quantify the local changes in the texture characteristics of the ultrasound off-line training images from both benign and malignant nodules. These features include: Fractal Dimension (FD), Local Binary Pattern (LBP), Fourier Spectrum Descriptor (FS), and Laws Texture Energy (LTE). The resulting feature vectors were used to build seven different classifiers: Support Vector Machine (SVM), Decision Tree (DT), Sugeno Fuzzy, Gaussian Mixture Model (GMM), K-Nearest Neighbor (KNN), Radial Basis Probabilistic Neural Network (RBPNN), and Naive Bayes Classifier (NBC). Subsequently, the feature vector-classifier combination that results in the maximum classification accuracy was used to predict the class of a new on-line test thyroid ultrasound image. Two data sets with 3D Contrast-Enhanced Ultrasound (CEUS) and 3D High Resolution Ultrasound (HRUS) images of 20 nodules (10 benign and 10 malignant) were used. Fine needle aspiration biopsy and histology results were used to confirm malignancy. Our results show that a combination of texture features coupled with SVM or Fuzzy classifiers resulted in 100% accuracy for the HRUS dataset, while GMM classifier resulted in 98.1% accuracy for the CEUS dataset. Finally, for each dataset, we have proposed a novel integrated index called Thyroid Malignancy Index (TMI) using the combination of FD, LBP, LTE texture features, to diagnose benign or malignant nodules. This index can help clinicians to make a more objective differentiation of benign/malignant thyroid lesions. We have compared and benchmarked the system with existing methods.

Automated oral cancer identification using histopathological images: A hybrid feature extraction paradigm

Oral cancer (OC) is the sixth most common cancer in the world. In India it is the most common malignant neoplasm. Histopathological images have widely been used in the differential diagnosis of normal, oral precancerous (oral sub-mucous fibrosis (OSF)) and cancer lesions. However, this technique is limited by subjective interpretations and less accurate diagnosis. The objective of this work is to improve the classification accuracy based on textural features in the development of a computer assisted screening of OSF. The approach introduced here is to grade the histopathological tissue sections into normal, OSF without Dysplasia (OSFWD) and OSF with Dysplasia (OSFD), which would help the oral onco-pathologists to screen the subjects rapidly. The biopsy sections are stained with H&E. The optical density of the pixels in the light microscopic images is recorded and represented as matrix quantized as integers from 0 to 255 for each fundamental color (Red, Green, Blue), resulting in a M × N × 3 matrix of integers. Depending on either normal or OSF condition, the image has various granular structures which are self similar patterns at different scales termed “texture”. We have extracted these textural changes using Higher Order Spectra (HOS), Local Binary Pattern (LBP), and Laws Texture Energy (LTE) from the histopathological images (normal, OSFWD and OSFD). These feature vectors were fed to five different classifiers: Decision Tree (DT), Sugeno Fuzzy, Gaussian Mixture Model (GMM), K-Nearest Neighbor (K-NN), Radial Basis Probabilistic Neural Network (RBPNN) to select the best classifier. Our results show that combination of texture and HOS features coupled with Fuzzy classifier resulted in 95.7% accuracy, sensitivity and specificity of 94.5% and 98.8% respectively. Finally, we have proposed a novel integrated index called Oral Malignancy Index (OMI) using the HOS, LBP, LTE features, to diagnose benign or malignant tissues using just one number. We hope that this OMI can help the clinicians in making a faster and more objective detection of benign/malignant oral lesions.

APPLICATION OF RECURRENCE QUANTIFICATION ANALYSIS FOR THE AUTOMATED IDENTIFICATION OF EPILEPTIC EEG SIGNALS

Epilepsy is a common neurological disorder that is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals are widely used to diagnose seizures. Because of the non-linear and dynamic nature of the EEG signals, it is difficult to effectively decipher the subtle changes in these signals by visual inspection and by using linear techniques. Therefore, non-linear methods are being researched to analyze the EEG signals. In this work, we use the recorded EEG signals in Recurrence Plots (RP), and extract Recurrence Quantification Analysis (RQA) parameters from the RP in order to classify the EEG signals into normal, ictal, and interictal classes. Recurrence Plot (RP) is a graph that shows all the times at which a state of the dynamical system recurs. Studies have reported significantly different RQA parameters for the three classes. However, more studies are needed to develop classifiers that use these promising features and present good classification accuracy in differentiating the three types of EEG segments. Therefore, in this work, we have used ten RQA parameters to quantify the important features in the EEG signals.These features were fed to seven different classifiers: Support vector machine (SVM), Gaussian Mixture Model (GMM), Fuzzy Sugeno Classifier, K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC), Decision Tree (DT), and Radial Basis Probabilistic Neural Network (RBPNN). Our results show that the SVM classifier was able to identify the EEG class with an average efficiency of 95.6%, sensitivity and specificity of 98.9% and 97.8%, respectively.

Orthogonal least square center selection technique – A robust scheme for multiple source Partial Discharge pattern recognition using Radial Basis Probabilistic Neural Network

Partial Discharge (PD) pattern recognition has emerged as a subject of vital interest for the diagnosis of complex insulation system of power equipment to personnel handling power system utilities and researchers alike, since the phenomenon serves inherently as an excellent non-intrusive testing technique. Recently, the focus of researchers has shifted to the recognition of defects in insulation due to multiple PD sources, as it is often encountered during real-time PD measurements. A survey of research literature indicates clearly that the recognition of fully overlapped PD patterns is yet an unresolved issue and that techniques such as Mixed Weibull Function, Neural Network (NN), Wavelet Transformation, etc. have been attempted with only reasonable success. Since most digital PD online acquisition systems record data for a stipulated and considerable duration as mandated by international standards, the database is large. This poses substantial complexity in classification during the training phase of the NNs. These difficulties may be attributed to ill-conditioned data, non-Markovian nature of discharges, curse of dimensionality of the data, etc. Since training methods based on random selection of centers from a large training set of fixed size are found to be relatively insensitive and detrimental to classification in many cases, a Forward Orthogonal Least Square algorithm (FOLS) is utilized in order to reduce the number of hidden layer neurons and obtain a parsimonious yet optimal set of centers. This algorithm, in addition, obviates the need for a separate clustering method making the procedure inherently viable for on-line PD recognition. This research work proposes a novel approach of utilizing Radial Basis Probabilistic Neural Network (RBPNN) with FOLS center selection algorithm for classification of multiple PD sources. Exhaustive analysis is carried out to ascertain the efficacy of classification of the proposed RBPNN-FOLS algorithm to cater to large training data set. A detailed comparison of the performance of the proposed scheme with that of the standard version of Probabilistic Neural Network (PNN) and Heteroscedastic PNN (HRPNN) that was taken up for study by the authors in their previous work indicates firstly the effectiveness of FOLS algorithm in obtaining parsimonious centers, points out secondly the capability of the Radial Basis Probabilistic Neural Network (RBPNN) model to integrate the advantages of the Radial Basis Function Neural Network (RBFNN) and PNN in classifying multiple PD sources and finally throws light on the exceptional capability of the FOLS-RBPNN in discriminating the sources of PD due to varying applied voltages also.

Intelligent Control of Flocculation Process Based on Radial Basis Probabilistic Neural Network for Sewage Treatment

The flocculating process of sewage treatment is a complicated and nonlinear system, and it is very difficult to found the process model to describe it. The radial basis probabilistic neural network (RBPNN) has the ability of strong function approach and fast convergence. In this paper, an intelligent optimized control system based on radial basis probabilistic neural network is presented. We constructed the structure of radial basis probabilistic neural network that used for controlling the flocculation process, and adopt the K-Nearest Neighbor algorithm and least square method to train the network. We given the architecture of control system and analyzed the working process of system. In this system, the parameters of flocculation process were measured using sensors, and then the control system can control the flocculation process real-time. The system was used in the sewage treatment plant. The experimental results prove that this system is feasible.