A Soft Computing Approach for Osteoporosis Risk Factor Estimation

Unnatural control chart patterns (CCPs) are associated with a particular set of assignable causes for process variation. Hence, effectively recognizing CCPs can substantially narrow down the set of possible causes to be examined, and accelerate the diagnostic search. Recently, machine-learning techniques, especially the artificial neural network (ANN), have been widely used as an effective tool for CCP recognition (CCPR) tasks. Most ANN applications in CCPR have been using static supervised ANNs, such as back propagation networks (BPNs) and learning vector quantization (LVQ) networks. The false recognition problem (i.e. the patterns are misclassified) commonly encountered for these ANN-based CCPR models is mainly due to the fact that the static ANNs cannot appropriately deal with dynamic patterns, such as CCPs. In this research, a dynamic training algorithm is designed to provide an LVQ network-based CCPR model the capability to on-line recognize the dynamic CCPs that vary over time. The numerical results using simulation show that the dynamically trained LVQ network-based model proposed in this research performs much better than other ANN-based models reported in literature with respective to recognition accuracy and speed. Although this research considers the specific application of a real-time CCPR model based on an LVQ network, the proposed dynamic training algorithm could be applied to CCPR systems based on other ANN architectures in general.

Artificial neural networks for the solution of the phase stability problem

1176 OUTCOME PREDICTION IN PATIENTS WITH BLADDER CANCER AFTER RADICAL CYSTECTOMY USING ARTIFICIAL NEURAL NETWORKS

Ecological Applications of Fuzzy Logic.- Ecological Applications of Qualitative Reasoning.- Ecological Applications of Non-supervised Artificial Neural Networks.- Ecological Applications of Genetic Algorithms.- Ecological Applications of Evolutionary Computation.- Ecological Applications of Adaptive Agents.- Bio-Inspired Design of Computer Hardware by Self-Replicating Cellular Automata.- Prediction and Elucidation of Stream Ecosystems.- Development and Application of Predictive River Ecosystem Models Based on Classification Trees and Artificial Neural Networks.- Modelling Ecological Interrelations in Running Water Ecosystems with Artificial Neural Networks.- Non-linear Approach to Grouping, Dynamics and Organizational Informatics of Benthic Macroinvertebrate Communities in Streams by Artificial Neural Networks.- Elucidation of Hypothetical Relationships between Habitat Conditions and Macroinvertebrate Assemblages in Freshwater Streams by Artificial Neural Networks.- Prediction and Elucidation of River Ecosystems.- Prediction and Elucidation of Population Dynamics of the Blue-green Algae Microcystis aeruginosa and the Diatom Stephanodiscus hantzschii in the Nakdong River-Reservoir System (South Korea) by a Recurrent Artificial Neural Network.- An Evaluation of Methods for the Selection of Inputs for an Artificial Neural Network Based River Model.- Utility of Sensitivity Analysis by Artificial Neural Network Models to Study Patterns of Endemic Fish Species.- Prediction and Elucidation of Lake and Marine Ecosystems.- A Comparison between Neural Network Based and Multiple Regression Models for Chlorophyll-a Estimation.- Artificial Neural Network Approach to Unravel and Forecast Algal Population Dynamics of Two Lakes Different in Morphometry and Eutrophication.- Hybrid Evolutionary Algorithm for Rule Set Discovery in Time-Series Data to Forecast and Explain Algal Population Dynamics in Two Lakes Different in Morphometry and Eutrophication.- Multivariate Time Series Prediction of Marine Zooplankton by Artificial Neural Networks.- Classification of Fish Stock-Recruitment Relationships in Different Environmental Regimes by Fuzzy Logic with Bootstrap Re-sampling Approach.- Computational Assemblage of Ordinary Differential Equations for Chlorophyll-a Using a Lake Process Equation Library and Measured Data of Lake Kasumigaura.- Classification of Ecological Images at Micro and Macro Scale.- Identification of Marine Microalgae by Neural Network Analysis of Simple Descriptors of Flow Cytometric Pulse Shapes.- Age Estimation of Fish Using a Probabilistic Neural Network.- Pattern Recognition and Classification of Remotely Sensed Images by Artificial Neural Networks.

Much has been reported in literature on virtual cell formation problems while a limited work is reported on worker assignments. Virtual Cellular Manufacturing Systems (VCMS) have come into existence, replacing traditional Cellular Manufacturing Systems (CMS), to meet highly dynamic production conditions in terms of demand, production lots, processing times, product mix and production sequences. Although the problem of worker assignment and flexibility in cell based manufacturing environments has been studied and analyzed in plenty using various heuristics/mathematical models, application of Artificial Neural Networks (ANN), adapted from the biological neural networks, is the recent development in this field exploiting the ability of ANN to work out mathematically-difficult-to-solve problems. In this attempt, the previous work of the author has been further developed and an attempt has been made to apply Learning Vector Quantization (LVQ) approach into worker assignment problems for higher order virtual cells i.e., three cells configurations and analyze the suitability of LVQ approach in terms of successful classification rate and simulation parameters for a number of VCMS periods.

NOVEL STAGING TOOL FOR LOCALIZED PROSTATE CANCER: A PILOT STUDY USING GENETIC ADAPTIVE NEURAL NETWORKS

Travel mode choice can be regarded essentially as a problem of pattern recognition. In the past, numerous researchers applied artificial neural network (ANN) to travel mode choice modelling. Feed-forward back propagation neural network (BPNN) is widely used, and most of the studies show that BPNN presents better prediction accuracy and generalization capacity. Probabilistic neural network (PNN) is also known as a general solution to pattern classification problems by following an approach developed in statistics. Comparing to BPNN, its shorter training time and better stability increase the reliability of simulation results, even so PNN is barely used in travel behaviour analysis. Therefore the paper tries to apply PNN for travel mode choice modelling. First, network structure is established based on the data obtained from resident trip survey, and then the K-means cluster algorithm is applied to optimize the hidden node number so that they can be modified dynamically. Moreover, the data centers and extended constants of gaussian radial basis function are changed adaptively during the learning progress, and the improved PNN is called KPNN. To reveal the superiority of KPNN, BPNN is also introduced to make a comparison. For BPNN, the optimal number of hidden nodes is recognized, and then the well trained network is applied. By proposing two performance measures, the classification and predictive capability of KPNN and BPNN is evaluated, it is found that KPNN outperforms BPNN on both the simplicity and prediction accuracy. Anyway, the simulation results show that KPNN can deal with the problem of travel choice modelling as well as, if not better than BPNN.

38 - Artificial Neural Network Applications in Power Electronics and Electrical Drives

This paper presents the application of artificial neural network (ANN) to predict deep beam deflection using experimental data from eight high-strength-self-compacting-concrete (HSSCC) deep beams. The optimized network architecture was ten input parameters, two hidden layers, and one output. The feed forward back propagation neural network of ten and four neurons in first and second hidden layers using TRAINLM training function predicted highly accurate and more precise load-deflection diagrams compared to classical linear regression (LR). The ANN's MSE values are 40 times smaller than the LR's. The test data R value from ANN is 0.9931; thus indicating a high confidence level.

Artificial neural network (ANN) is a simplified model of the biological nervous system consisting of nerve cells or neurons. The application of ANN to food process engineering is relatively novel. ANN had been employed in diverse applications like food safety and quality analyses, food image analysis, and modeling of various thermal and non-thermal food-processing operations. ANN has the ability to map nonlinear relationships without any prior knowledge and predicts responses even with incomplete information. Every neural network possesses data in the form of connection weights interconnecting lines between the input to hidden layer neurons and weights of hidden to output layer neurons, which has a significant role in predicting the output data. The applications of ANN in different unit operations in food processing were described that includes theoretical developments using intelligent characteristics for adaptability, automatic learning, classification, and prediction. The parallel architecture of ANN resulted in a fast response and low computational time making it suitable for application in real-time systems of different food process operations. The predicted responses obtained by the ANN model exhibited high accuracy due to lower relative deviation and root mean squared error and higher correlation coefficient. This paper presented the various applications of ANN for modeling nonlinear food engineering problems. The application of ANN in the modeling of the processes such as extraction, extrusion, drying, filtration, canning, fermentation, baking, dairy processing, and quality evaluation was reviewed. HIGHLIGHTS 1. This paper discusses application of ANN in different emerging trends in food process. 2. Application of ANN to develop non-linear multivariate modeling is illustrated. 3. ANNs have been shown to be useful tool for prediction of outcomes with high accuracy. 4. ANN resulted in fast response making it suitable for application in real time systems.

Speech can be represented phonetically by a finite set of symbols called the phonemes of the language. Identification of these phonemes is the most important part of any speech recognition system. A work is described in this paper where an Artificial Neural Network (ANN) based approach is carried out to recognize consonant and vowel phonemes of two alphabet Assamese words. A Self Organizing Map (SOM) based segmentation algorithm segments the incoming speech signal into its constituent phonemes and from the SOM segmented phonemes the three constituent phonemes are identified by some sort of recognition algorithm based on two different ANN structures Probabilistic Neural Network (PNN) and Learning Vector Quantization (LVQ). Formant frequency of Assamese phonemes is used very effectively as a priori knowledge in the proposed algorithm. The SOM based segmentation algorithm shows distinct advantage in terms of recognition success rate in comparison to the conventional speech segmentation methods like windowing or Discrete Wavelet Transform (DWT).

This article discusses the applications of Artificial Neural Network (ANN) and Fuzzy Logic Control (FLC) strategies in a control context of a grid connected Wind Energy Conversion System (WECS), using a Doubly-Fed Induction Generator (DFIG). To rigorously explore the performance of the controllers, a systemic approach based on an ideal model and a nominal model of the wind turbine is used for the comparative analysis between PI, FLC and ANN. It turns out that the PI is less efficient than ANN and FLC. It turns out that the PI is less efficient than ANN and FLC. Whereas, for a high level of control, the FLC has better performance than the ANN. For a low level, the performance of the ANN is very slightly superior to the FLC. In addition, one notes a good rejection of disturbance and a good robustness with the FLC compared to the ANN. The latter has a Total Harmonic Distortion (THD) and cos ϕ ≈ 1 slightly better than the FLC. In short, the ANN and FLC present great advantages for the WECS and the results obtained are satisfactory. The platform used for modeling and simulation studies is MATLAB/Simulink.KeywordsWind Energy Conversion SystemFuzzy Logic Control (FLC)Artificial Neural Network (ANN)DFIGPower grid

This paper presents one feature based method for automatic classification and recognition of 7 digital modulations for software defined radio. After reviewing some spectral based features, new statistical based ones are proposed. The classification is conducted with artificial neural networks (ANN). Three architectures are investigated: Multilayer Perceptron (MLP) with one and two hidden layers and Probabilistic Neural Network (PNN). Simulation results for SNR levels of 0, 5, 8, 10 dB are shown. The simulation as well as comparison of these three architectures reveals that MLP with two hidden layers exhibits best classification results with 95% success rate at 5 dB SNR level, while all of them correctly classify in over 98% at 10 dB SNR.

An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. The ANN has recently been applied in process control, identification, diagnostics, character recognition, sensory prediction, robot vision, and forecasting. In Textiles and Clothing industries, it involves the interaction of a large number of variables. Because of the high degree of variability in raw materials, multistage processing and a lack of precise control on process parameters, the relation between such variables and the product properties is relied on the human knowledge but it is not possible for human being to remember all the details of the process-related data over the years. As the computing power has substantially improved over last decade, the ANN is able to learn such datasets to reveal the unknown relation between various variables effectively. Therefore, the application of ANN is more widespread in textiles and clothing industries over last decade. In this chapter, it aims to review current application of ANN in textiles and clothing industries over last decade. Based on literature reviews, the challenges encountered by ANN used in the industries will be discussed and the potential future application of ANN in the industries will also be addressed. The structure of this chapter comprises of seven sections. The first section includes background of ANN, importance of ANN in textiles and clothing and the arrangement of this chapter. In forthcoming three sections, they include review of applications of ANN in fibres and yarns, in chemical processing, and in clothing over last decade. Afterwards, challenges encountered by ANN used in textiles and clothing industries will be discussed and potential future application of ANN in textiles and clothing industries will be addressed in last section.

The application of artificial neural networks to the estimation of bioprocess variables will be discussed. In fermentation processes, direct on-line measurements of primary process variables usually are unavailable. The state of the cultivation, therefore, has to be inferred from measurements of secondary variables and any previous knowledge of process dynamics. This research investigates the learning, recall and generalization characteristics of neural networks trained to model the nonlinear behavior of a fermentation process. Two different neural network methodologies are discussed, namely, feed-forward and recurrent neural networks, which differ in their treatment of time dependence. The neural networks are trained by backpropagation using a conjugate gradient technique, which provides a dramatic improvement in the convergence speed. The objective is to use environmental and physiological information available from on-line sensors to estimate concentrations of species in a bioreactor. Results of the neural network estimators are presented, based on experimental data available from the ethanol production by Zymomonas mobilis fermentation. The feed-forward and recurrent neural network methodologies are demonstrated to perform suitably as unmeasurable state estimators. Both networks offer comparable abilities of recall, but recurrent networks perform better than feed-forward networks in generalization.