# SOFT COMPUTING TECHNIQUES BY SIVANANDAM PDF

wiley, In this book the basic concepts of soft computing are dealt in detail with Fuzzy logic techniques have been clearly dealt with suitable examples. By ( author) S.N. SIVANANDAM, S.N. DEEPA. 0 stars out of 5 (0 rating). Format: eBook. Fuzzy logic techniques have been clearly dealt with suitable examples. Special Features: Dr. S. N. Sivanandam has published 12 books· He has delivered. To become familiar with neural networks that can learn from available examples and generalize to Introduction to Optimization Techniques. Derivative cittadelmonte.infondam, cittadelmonte.info "Principles of Soft Computing" Second Edition, Wiley.

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Principles of soft computing by Sivanandam and Deepa second edition here is the Where can I download the Principles of Soft Computing PDF by Sivanandam and . Fuzzy logic techniques have been clearly dealt with suitable examples. Soft computing cittadelmonte.info - TEXT BOOKS 1. Principlesof Soft Computing by S. N. Sivanandam and S. N. Deepa, Wiley India Edition. 2. Among the evolutionary techniques, the genetic algorithms (GAs) are the most . Neural Network, Fuzzy Logic, Genetic Algorithm, Digital Control, Adaptive and.

Would you like to tell us about a lower price? In this book the basic concepts of soft computing are dealt in detail with the relevant information and knowledge available for understanding the computing process. The various neural network concepts are explained with examples, highlighting the difference between various architectures. Fuzzy logic techniques have been clearly dealt with suitable examples. Genetic algorithm operators and the various classifications have been discussed in lucid manner, so that a beginner can understand the concepts with minimal effort. The book can be used as a handbook as well as a guide for students of all engineering disciplines, soft computing research scholars, management sector, operational research area, computer applications and for various professionals who work in this area. Read more Read less.

The various neural network concepts are explained with examples, highlighting the difference between various architectures. Fuzzy logic techniques have been clearly dealt with suitable examples. Genetic algorithm operators and the various classifications have been discussed in lucid manner, so that a beginner can understand the concepts with minimal effort.

The book can be used as a handbook as well as a guide for students of all engineering disciplines, soft computing research scholars, management sector, operational research area, computer applications and for various professionals who work in this area. Read more Read less. English Format: Visit Exam Central to find eBooks, solved papers, tips and more for all exams.

See More. Customers who bought this item also bought. Page 1 of 1 Start over Page 1 of 1. Introduction to Automata Theory, Languages, and Computation: Pearson New International Edition.

John E. Soft Computing: Neuro-Fuzzy and Genetic Algorithms. Artificial Neural Networks. Mining of Massive Datasets. Product description Product Description In this book the basic concepts of soft computing are dealt in detail with the relevant information and knowledge available for understanding the computing process.

He has a total teaching experience UG and PG of 41 years. The total number of undergraduate and postgraduate projects guided by him for both Computer Science and Engineering and Electrical and Electronics Engineering is around She was a gold medalist in her BE Degree Program. She has received G. She has published 4 books and papers in International and National Journals. Product details Format: Kindle Edition File Size: Wiley; 2 edition 13 October Sold by: English ASIN: Not enabled X-Ray: Not Enabled.

Customers who viewed this item also viewed. Principles of Soft Computing, 3ed. Deepa S. Share your thoughts with other customers. Write a product review. Top Reviews Most recent Top Reviews. There was a problem filtering reviews right now. Please try again later. It represents the measure of the aspect ratio. It is imaging [56] and recently also in image search over the obtained from the ratio of the minor axis to the major axis in Internet [57, 58].

There are many different implementations of CBIR. It is defined as the ratio of perimeters of the convex Nevertheless, the key to a good retrieval system is to choose hull over that of the original contour. It can be used as a single performance algorithms when raw data are processed by different feature measure: In order to provide a proof of concept, we used the resulting procedures to classify the flaws detected by the EC testing.

Ten-fold cross-validation the probability that a classifier will rank a randomly The classification performance of each classifier is chosen positive instance higher than a randomly evaluated by using the ten-fold cross-validation method [64], a chosen negative one.

Accordingly, all the available data, belonging to the different defects, have been randomly divided into 10 disjoint subsets MCC Matthews Correlation Coefficient that folders , each containing approximately the same amount of correlates the observed and predicted binary instances.

In each experiment, nine folders have been used as classifications by simultaneously considering true and training data, i. It can assume a value folder was used as validation, i. This process was repeated 10 times, for each different prediction, 0 no better than random prediction and -1 choice of validation folder. The 10 results were then averaged indicates total disagreement between prediction and to produce a single estimation.

Performance measures Given a binary classifier and an instance, there are four possible outcomes. If the instance is positive and it is classified C.

Sample data as positive, it is counted as a true positive TP ; if it is Given the intended use on FRA materials, the sample data classified as negative, it is counted as a false negative FN. If used in the study refer to a subset of a known database of EC the instance is negative and it is classified as negative, it is signal samples for aluminum aircraft structures [67].

The counted as a true negative TN ; if it is classified as positive, it overall database is divided in 4 parts. Given a classifier and a set of instances the test set , a two-by-two confusion matrix also The first part 1 contains records acquired on an called a contingency table can be constructed representing the aluminum sample with notches of width 0.

This matrix forms the basis 0. Nevertheless, there is no general with an angle of 30 degrees, 0. Following, the most common metrics are defined The second part 2 refers to records, notches of width [66]: The third part 3 refers to two-layer aluminum aircraft structure with rivets, two notches below the rivets in the first layer width 0.

TPR , that measures the portion of actual positives which are correctly identified as such: The latter part 4 refers to four-layer aluminum structure layer thickness 2,5 mm with rivets containing 4 notches width 0.

In this paper we used two dataset belonging to the part 1. The first dataset Set 1 includes only two set of samples acquired on the aluminum structure. The first set refers to the notch perpendicular of width 0. The Precision also called positive predictive value , that is a measure of actual positives with respect to all the 1 ROC curves are two-dimensional graphs in which Sensitivity is plotted on instances classified as positive: A ROC graph depicts relative trade-offs between benefits true positives and costs false positives.

The second dataset Set 2 II, we used two set of data, each one of 40 feature vectors, includes the entire part 1. It contains twelve types of defects forming the positive and the negative instances required to train classes.

Each class includes 20 signals. Each signal is composed by BRAIN algorithm, reporting the maximum value for all the samples, acquired at a sampling frequency of 10 kHz for each measures. For each frequency class we valued In the Table, the underscore sign means a literal in negated the minimum, the maximum, the average and the median of the form [15]. FFT module. The level ranges were Ten fold cross validation results adaptively chosen by considering the dynamic range centered Test Training Validation around the median.

Amplitude Spectrum of the two datasets belonging to the Set 1. The second refers to the notch oblique of width 0. J48 0. In each experiment we compared each Bayes Multilayer defect class to all the others. In this way we have 12 different 0. The mean performance of the classifiers, C4. The table highlights a dramatic decrease Then, a LDA was applied on these vectors, obtaining a single in performance: Wavelet-based Experiments In this approach, the wavelet coefficients of the EC signals provide a compact representation that shows the energy distribution of the EC signal in time and frequency.

These coefficients represent the feature vectors. Plot of the LDA based feature vectors for the two classes of the Set 1. Class 1 represents the notch perpendicular of width 0. Class 2 In order to reduce the feature vector dimension, we applied the represents the notch oblique of width 0.

Then, statistics over the obtained data were calculated. Accordingly, the feature extraction was The one-dimension feature vectors obtained were used as the accomplished by using both the SAP and MAV values. The performance results are summarized in Table IV. So, with a scale ranging from 1 to and data samples, we had a feature vector of elements Ten-fold cross validation results means Classifier for SAP and for MAV for each raw signal.

In Fig.

## Text books 1 principles of soft computing s n

The results were worse than before. Scatter graph of the first three PDA-based feature vectors for the 2 classes belonging to the Set 1. The results, depicted in Table V, are also unsatisfactory. In this experiment the scale-samples matrix of the wavelet J48 0. Ten-fold cross validation results means Classifier Acc. The level of PWT was determined through a trial and error J48 0. Then PCA was used as Multilayer dimension reduction method.

The resulting feature vector 0. Ten-fold cross validation results means Classifier Each coefficient was represented by the couple made up by its Acc. MAV and variance. In this way we obtained a dimension J48 0. Naive 0. The resulting 3- 0. Overall, the Wavelet-based methods do not appear to grasp the invariant characteristics signatures of each class with respect to the other defects considered.

CBIR-based Experiments In EC signals the presence of damage is characterized by a particular output probe impedance, resulting in a specific shape in the complex plane. We used the shape of the impedance in the complex plane to identify defects.

Tridimensional scatter graph of the proposed feature vectors for 12 classes Set 2. The Set 1 is represented by class 1 and class 2. Typical shape of coil impedance in the complex plane. Multilayer 0. This is also confirmed by the spectrum analysis of each single channel of the samples.

Figures show, for each adopted performance measure, a summary of the results As evidenced by the tridimensional scatter graph reported Y-axis obtained by varying the feature extraction methods for in Fig.

J48 N. Sensitivity values for different features extraction methods and soft correlation coefficients. DWT led to acceptable results for J48 computing based algorithms — Set 1. Specificity values for different features extraction methods and soft classifiers. Accuracy values for different features extraction methods and soft Fig. Precision values for different features extraction methods and soft computing based algorithms — Set 1. Nevertheless, 0,97 Methods 1,00 the low values of the Precision Fig.

It outperformed all the other 0,40 e methods for each classifier applied. Matthews correlation coefficients for different features extraction defect classification.

AUC scores for different features extraction methods and soft computing based algorithms — Set 1. Accuracy values for different features extraction methods and soft computing based algorithms — Set 2. F-Measure values for different features extraction methods and soft computing based algorithms — Set 1. Sensitivity values for different features extraction methods and soft computing based algorithms — Set 2.

Specificity values for different features extraction methods and soft Fig. AUC scores for different features extraction methods and soft computing based algorithms — Set 2. Precision values for different features extraction methods and soft Fig.

F-Measure values for different features extraction methods and soft computing based algorithms — Set 2. This study has M 0,70 FFT addressed two among the main issues in aerospace structure 0,63 a 0,60 defects classification: Matthews correlation coefficients for different features extraction The performance of the resulting detection systems have methods and soft computing based algorithms — Set 2.

While there are positive instances ui T geometric parameters, evidenced itself as the most effective.

Repetition Deletion largely used in the literature, showed a quite limited behavior 2. Build Sij sets from T 2.

While there are elements in Sij The results of this study have evidenced that the key to a 2. Compute the Rij relevances successful soft-computing based testing system is to choose the 2.

Compute the Ri relevances right feature extraction method, representing the defect as 2. Compute the R relevances accurately and uniquely as possible in a short time. Update term: Update Sij sets algorithm has the further advantage to showing explicitly the 2.

Add term m to f: Compared to other works on the 2. Update negative instances have shown better results. Check consistency Open problems rest in the validation of the results using larger datasets, even of FRA materials, and in the extension of The inner cycle refers to the selection of the literals of each the results to other NDT techniques as ultrasound and formula term, while the outer one is devoted to the terms thermography, and this will be matter of a future work.

Splice as: Starting from these sets Sij, the algorithm using fuzzy sets [75], in order to infer a DNF formula that is determines for each literal xk belonging to them a set of consistent with a given set of data which may have missing coefficients Rij, Ri and R, called relevances, forming a bits.

The conjunctive terms of the formula are computed in an iterative way by identifying, from the given data, a family of sets of conditions that must be satisfied by all the positive instances and violated by all the negative ones; such conditions A.

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*OPTIMIZATION TECHNIQUES BY SS RAO PDF*

This allows the selection of the literals on a maximum probability greedy The data instances in T are divided into two classes, named criteria the literal having maximum relevance value is positive and negative, respectively modeled by the n-sized selected.

The conjunctive terms of the formula are carried-out literal itself. The inner cycle is then repeated and the term is in an iterative way by two nested loops see algorithm schema.

Then the new term is added to the formula and, in [2] A. IHS White Paper. Then, the inner cycle starts again on the remaining data. Owen, S. Gardner, B. Modrzejewski, J.

Fetty, K.

Karg, "Improving The algorithm ends when there are no more data to treat. In fact, it [4] W. Hou, W. Song, C. He, Z. Liu, Y. Huang, B. Wu, "Measurement of elastic constants of limited-size piezoelectric ceramic sample by ultrasonic reduction step, whose task is limiting the presence of missing method," Measurement, Journal of the International Measurement bits, by recovering them as possible. Such redundancy is Confederation, Vol. Meshreki, H. Attia,"Monitoring and Control of Machining deleting all the repetitions, in order to avoid consistency Process by Data Mining and Pattern Recognition," Proceedings of the violation that can halt the process.

Chen Ed. Springer, Berlin. So, storing and computing for large data [8] V. Gileva, "Application of linear recognition methods in in a computer is space and time consuming. Once these rules are available the [9] M. Jalal, "Soft computing techniques for compressive strength detection activity is extremely simple and fast and hence can be prediction of concrete cylinders strengthened by CFRP composites," Science and Engineering of Composite Materials, Vol.

December In order to overcome the limitations related to high [10] X. Yan-hong, Z. Ze, L. Kun and Z. Mathematical and programming China August Meyer, F. Leisch, K. Programming model [78] has been adopted. Finally, in order to [12] W. Chun, L. Overall, the results obtained on standard data Vol. Sumathi, S. Sivanandam, "Introduction to Data Mining and Its faster than the serial one. Moreover, increasing the problem Applications," Springer edition, Michalski, J.

Carbonell and T. Mitchell, "Machine Learning: An increases. Nardone, S. Rampone ed. Scientific, Fayyad, "Data mining and knowledge discovery in databases: Smith, "The Use of composites in aerospace: Past, present and future [19] G. Springer, pp. Augusteijn, B.

Rioul, M. John, P. Liang, P. Rampone, C. Coifman and M. Wickerhauser, "Entropy-based algorithms for [23] Ian H. Witten, Eibe Frank, Mark A. Theory, Vol. Mendelson, Introduction to Mathematical Logic. Sasi, B. Rao, S. Thirunavukkarasu, T.

Jayakumar and P. London, p. Kalyanasundaram, "Wavelet transform based method for eddy current [25] F. Darema, "The spmd model: Leavey, M.

## PRINCIPLES OF SOFT COMPUTING (With CD ) - cittadelmonte.infondam & cittadelmonte.info - Google книги

James, J. Summerscales and R.

Sutton, "An [26] J. Leichtl, P. Crandall, M. Clement, "Parallel programming in multi- introduction to wavelet transforms: Pei, H. Liang, Y. Qia, "A frequency spectrum analysis method for eddy current nondestructive testing," Proceedings of the [48] J. Chu , I. Moon , Y. Lee , S. Kim and S. Mechatronics, Vol. Tian, A. Sophian, D. Taylor, J. Yin, S. Measurement Technology, Vol. Phinyomark, A. Nuidod, P. Phukpattaranont, C. Nuruzzaman, O. Boyraz, B. Swain and D.

Journal Computer [32] I. Daubechies, "The wavelet transform, time-frequency localization and Vision, Vol. Man and Cyb. Gudivada, V. Management, Vol. Pan, Y. He, G. Tian, D. Chen, F. Kokare, B. Chatterji, P. Biswas, "Comparison of similarity Measurement, Vol. Kshirsagar, M. Baviskar, and M.

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