scispace - formally typeset
Search or ask a question

Showing papers on "Soft computing published in 2009"


Journal ArticleDOI
TL;DR: This paper surveys more than 100 related published articles that focus on neural and neuro-fuzzy techniques derived and applied to forecast stock markets to show that soft computing techniques are widely accepted to studying and evaluating stock market behavior.
Abstract: The key to successful stock market forecasting is achieving best results with minimum required input data. Given stock market model uncertainty, soft computing techniques are viable candidates to capture stock market nonlinear relations returning significant forecasting results with not necessarily prior knowledge of input data statistical distributions. This paper surveys more than 100 related published articles that focus on neural and neuro-fuzzy techniques derived and applied to forecast stock markets. Classifications are made in terms of input data, forecasting methodology, performance evaluation and performance measures used. Through the surveyed papers, it is shown that soft computing techniques are widely accepted to studying and evaluating stock market behavior.

714 citations


Proceedings ArticleDOI
27 Oct 2009
TL;DR: In this article, the main aim is to understand the soft computing approach to image segmentation, which is an emerging field that consists of complementary elements of fuzzy logic, neural computing and evolutionary computation.
Abstract: Soft Computing is an emerging field that consists of complementary elements of fuzzy logic, neural computing and evolutionary computation. Soft computing techniques have found wide applications. One of the most important applications is image segmentation. The process of partitioning a digital image into multiple regions or sets of pixels is called image segmentation. Segmentation is an essential step in image processing since it conditions the quality of the resulting interpretation. Lots of approaches have been proposed and a dense literature is available In order to extract as much information as possible from an environment, multicomponent images can be used. In the last decade, multicomponent images segmentation has received a great deal of attention for soft computing applications because it significantly improves the discrimination and the recognition capabilities compared with gray-level image segmentation methods. In this paper, the main aim is to understand the soft computing approach to image segmentation.

371 citations


01 Jan 2009
TL;DR: The main aim is to understand the soft computing approach to image segmentation, which significantly improves the discrimination and the recognition capabilities compared with gray-level image segmentations methods.
Abstract: Soft Computing is an emerging field that consists of complementary elements of fuzzy logic, neural computing and evolutionary computation. Soft computing techniques have found wide applications. One of the most important applications is edge detection for image segmentation. The process of partitioning a digital image into multiple regions or sets of pixels is called image segmentation. Edge is a boundary between two homogeneous regions. Edge detection refers to the process of identifying and locating sharp discontinuities in an image. In this paper, the main aim is to survey the theory of edge detection for image segmentation using soft computing approach based on the Fuzzy logic, Genetic Algorithm and Neural Network.

349 citations


Journal ArticleDOI
Sukhyun Yun1, Jaehun Lee1, Wooyong Chung1, Euntai Kim1, Soohan Kim2 
TL;DR: The two schemes introduced in this paper exhibit range-free localization, which utilize the received signal strength from the anchor nodes, and approximate the entire sensor location mapping from the anchored node signals by a neural network.
Abstract: In this paper, we propose two intelligent localization schemes for wireless sensor networks (WSNs) The two schemes introduced in this paper exhibit range-free localization, which utilize the received signal strength (RSS) from the anchor nodes Soft computing plays a crucial role in both schemes In the first scheme, we consider the edge weight of each anchor node separately and combine them to compute the location of sensor nodes The edge weights are modeled by the fuzzy logic system (FLS) and optimized by the genetic algorithm (GA) In the second scheme, we consider the localization as a single problem and approximate the entire sensor location mapping from the anchor node signals by a neural network (NN) The simulation and experimental results demonstrate the effectiveness of the proposed schemes by comparing them with the previous methods

183 citations


Journal ArticleDOI
TL;DR: Model trees as a new soft computing method was invoked for prediction of significant wave height and error statistics of model trees and feed-forward back propagation (FFBP) ANNs were similar, while model trees was marginally more accurate.

164 citations


Journal ArticleDOI
TL;DR: Comparisons indicate that the error statistics of SVM model marginally outperforms ANN even with much less computational time required, which shows that SVM can be successfully used for prediction of Hs.

159 citations


Journal ArticleDOI
TL;DR: Three soft computing techniques viz; fuzzy logic, neural network, and genetic algorithm are used to design alternative control schemes for switching the APF and a comparative study of the results obtained is presented.
Abstract: Nonmodel-based controllers have been explored for the control of a shunt active power filter (APF) designed for harmonic and reactive current mitigation. In this paper, three soft computing techniques viz; fuzzy logic, neural network, and genetic algorithm are used to design alternative control schemes for switching the APF. The models for these control schemes are designed and simulated in MATLAB. A comparative study of the results obtained using these artificial-intelligence-based schemes is presented.

158 citations


Journal ArticleDOI
TL;DR: The performance of a hybrid methodology combining feed forward CNN, fuzzy logic and genetic algorithm to forecast one-day ahead daily water demands at irrigation districts considering that only flows in previous days are available for the calibration of the models were analysed.

135 citations


Journal ArticleDOI
TL;DR: The research results indicate that the proposed approach can effectively help engineers determine optimal process parameter settings and achieve competitive advantages of product quality and costs.
Abstract: Determining optimal process parameter settings critically influences productivity, quality, and cost of production in the plastic injection molding (PIM) industry. Previously, production engineers used either trial-and-error method or Taguchi's parameter design method to determine optimal process parameter settings for PIM. However, these methods are unsuitable in present PIM because the increasing complexity of product design and the requirement of multi-response quality characteristics. This research presents an approach in a soft computing paradigm for the process parameter optimization of multiple-input multiple-output (MIMO) plastic injection molding process. The proposed approach integrates Taguchi's parameter design method, back-propagation neural networks, genetic algorithms and engineering optimization concepts to optimize the process parameters. The research results indicate that the proposed approach can effectively help engineers determine optimal process parameter settings and achieve competitive advantages of product quality and costs.

130 citations


Journal ArticleDOI
TL;DR: This work discusses temporal fuzzy confidence curves for the common elderly abnormal activity of falling and proposes a new application of fuzzy logic in a novel approach to modeling and monitoring human activity, in particular, the well-being of an elderly resident, from video.
Abstract: As part of an interdisciplinary collaboration on elder-care monitoring, a sensor suite for the home has been augmented with video cameras. Multiple cameras are used to view the same environment and the world is quantized into nonoverlapping volume elements (voxels). Through the use of silhouettes, a privacy protected image representation of the human acquired from multiple cameras, a 3-D representation of the human is built in real time, called voxel person. Features are extracted from voxel person and fuzzy logic is used to reason about the membership degree of a predetermined number of states at each frame. Fuzzy logic enables human activity, which is inherently fuzzy and case-based, to be reliably modeled. Membership values provide the foundation for rejecting unknown activities, something that nearly all current approaches are insufficient in doing. We discuss temporal fuzzy confidence curves for the common elderly abnormal activity of falling. The automated system is also compared to a ground truth acquired by a human. The proposed soft computing activity analysis framework is extremely flexible. Rules can be modified, added, or removed, allowing per-resident customization based on knowledge about their cognitive and functionality ability. To the best of our knowledge, this is a new application of fuzzy logic in a novel approach to modeling and monitoring human activity, in particular, the well-being of an elderly resident, from video.

115 citations




Journal ArticleDOI
01 Jun 2009
TL;DR: This study detects which methodology or methodologies of SC are used frequently together to solve the special problems of medicine and introduces briefly the various SC methodologies and presents various applications in medicine between the years 2000 and 2008.
Abstract: Soft computing (SC) is not a new term; we have gotten used to reading and hearing about it daily. Nowadays, the term is used often in computer science and information technology. It is possible to define SC in different ways. Nonetheless, SC is a consortium of methodologies which works synergistically and provides, in one form or another, flexible information processing capability for handling real life ambiguous situations. Its aim is to exploit the tolerance for imprecision, uncertainty, approximate reasoning and partial truth in order to achieve tractability, robustness and low-cost solutions. SC includes fuzzy logic (FL), neural networks (NNs), and genetic algorithm (GA) methodologies. SC combines these methodologies as FL and NN (FL-NN), NN and GA (NN-GA) and FL and GA (FL-GA). Recent years have witnessed the phenomenal growth of bio-informatics and medical informatics by using computational techniques for interpretation and analysis of biological and medical data. Among the large number of computational techniques used, SC, which incorporates neural networks, evolutionary computation, and fuzzy systems, provides unmatched utility because of its demonstrated strength in handling imprecise information and providing novel solutions to hard problems. The aim of this paper is to introduce briefly the various SC methodologies and to present various applications in medicine between the years 2000 and 2008. The scope is to demonstrate the possibilities of applying SC to medicine-related problems. The recent published knowledge about use of SC in medicine is researched in MEDLINE. This study detects which methodology or methodologies of SC are used frequently together to solve the special problems of medicine. According to MEDLINE database searches, the rates of preference of SC methodologies in medicine were found as 68% of FL-NN, 27% of NN-GA and 5% of FL-GA. So far, FL-NN methodology was significantly used in medicine. The rates of using FL-NN in clinical science, diagnostic science and basic science were found as %83, %71 and %48, respectively. On the other hand NN-GA and FL-GA methodologies were mostly preferred by basic science of medicine. Another message emerging from this survey is that the number of papers which used NN-GA methodology has continuously risen until today. Also search results put the case clearly that FL-GA methodology has not applied well enough to medicine yet. Undeniable interest in studying SC methodologies in genetics, physiology, radiology, cardiology, and neurology disciplines proves that studying SC is very fruitful in these disciplines and it is expected that future researches in medicine will use SC more than it is used today to solve more complex problems.

Journal ArticleDOI
TL;DR: The main advantage of this approach is the sufficient interpretability and transparency of the proposed FCM model, which make it a convenient consulting tool in describing cotton yield behavior.
Abstract: The management of cotton yield behavior in agricultural areas is a very important task because it influences and specifies the cotton yield production. An efficient knowledge-based approach utilizing the method of fuzzy cognitive maps (FCMs) for characterizing cotton yield behavior is presented in this research work. FCM is a modelling approach based on exploiting knowledge and experience. The novelty of the method is based on the use of the soft computing method of fuzzy cognitive maps to handle experts' knowledge and on the unsupervised learning algorithm for FCMs to assess measurement data and update initial knowledge. The advent of precision farming generates data which, because of their type and complexity, are not efficiently analyzed by traditional methods. The FCM technique has been proved from the literature efficient and flexible to handle experts' knowledge and through the appropriate learning algorithms can update the initial knowledge. The FCM model developed consists of nodes linked by directed edges, where the nodes represent the main factors in cotton crop production such as texture, organic matter, pH, K, P, Mg, N, Ca, Na and cotton yield, and the directed edges show the cause-effect (weighted) relationships between the soil properties and cotton field. The proposed method was evaluated for 360 cases measured for three subsequent years (2001, 2003 and 2006) in a 5ha experimental cotton yield. The proposed FCM model enhanced by the unsupervised nonlinear Hebbian learning algorithm, was achieved a success of 75.55%, 68.86% and 71.32%, respectively for the years referred, in estimating/predicting the yield between two possible categories (''low'' and ''high''). The main advantage of this approach is the sufficient interpretability and transparency of the proposed FCM model, which make it a convenient consulting tool in describing cotton yield behavior.

Journal ArticleDOI
TL;DR: Results show great potential in the use of the FNN for real-time driver identification and verification and the profiling of driver behaviors has numerous other potential applications for use by law enforcement and companies dealing with buses and truck drivers.
Abstract: Advancements in biometrics-based authentication have led to its increasing prominence and are being incorporated into everyday tasks. Existing vehicle security systems rely only on alarms or smart card as forms of protection. A biometric driver recognition system utilizing driving behaviors is a highly novel and personalized approach and could be incorporated into existing vehicle security system to form a multimodal identification system and offer a greater degree of multilevel protection. In this paper, detailed studies have been conducted to model individual driving behavior in order to identify features that may be efficiently and effectively used to profile each driver. Feature extraction techniques based on Gaussian mixture models (GMMs) are proposed and implemented. Features extracted from the accelerator and brake pedal pressure were then used as inputs to a fuzzy neural network (FNN) system to ascertain the identity of the driver. Two fuzzy neural networks, namely, the evolving fuzzy neural network (EFuNN) and the adaptive network-based fuzzy inference system (ANFIS), are used to demonstrate the viability of the two proposed feature extraction techniques. The performances were compared against an artificial neural network (NN) implementation using the multilayer perceptron (MLP) network and a statistical method based on the GMM. Extensive testing was conducted and the results show great potential in the use of the FNN for real-time driver identification and verification. In addition, the profiling of driver behaviors has numerous other potential applications for use by law enforcement and companies dealing with buses and truck drivers.

Journal ArticleDOI
TL;DR: It is shown that the solution derived from the IWFNLP method satisfies the decision maker's desirable achievement level of the profit objective, ROII objective and shortage cost constraint goal under the desirable possible level of fuzzy demand.

Journal ArticleDOI
01 Jan 2009
TL;DR: It has been ascertained from the present investigation that the proposed soft-computing-based controller works effectively in the tracking control of such a multi-link flexible manipulator.
Abstract: The paper describes use of soft computing methods (fuzzy logic and neural network techniques) in the development of a hybrid fuzzy neural control (HFNC) scheme for a multi-link flexible manipulator. A manipulator with multiple flexible links is a multivariable system of considerable complexity due to the inter-link coupling effects that are present in both rigid and flexible motions. Modelling and controlling the dynamics of such manipulators is therefore difficult. The proposed HFNC scheme generates control actions combining contributions form both a fuzzy controller and a neural controller. The primary loop of the proposed HFNC contains a fuzzy controller and a neural network controller in the secondary loop to compensate for the coupling effects due to the rigid and flexible motion along with the inter-link coupling. It has been ascertained from the present investigation that the proposed soft-computing-based controller works effectively in the tracking control of such a multi-link flexible manipulator. The results are extendable to other multivariable systems of similar complexity.

Journal ArticleDOI
TL;DR: The bullwhip effect is measured from the transfer function model and the effect can be reduced by applying soft computing approach, which helps the managers to forecast the demand with less distortion and to improve the supply chain effectiveness.
Abstract: The information transferred in the form of orders between the nodes of a supply chain tends to be distorted when it moves from downstream to upstream. This phenomenon is called as bullwhip effect and this research is aimed to analyze this effect deeply in a single input single output (SISO) model. A discrete time series SISO model is developed for the analysis and it proves to be very useful in revealing the dynamics characteristics of the system. The bullwhip effect is measured from the transfer function model and the effect can be reduced by applying soft computing approach. A detailed sensitivity analysis is carried out to investigate the behavior of the model under various conditions. The applied fuzzy logic theory controls the errors and change in errors associated with forecasted demand between the nodes of a supply chain and it allows a smooth information flow in the chain. Tuning of fuzzy logic controller has been performed using adaptive neuro-fuzzy inference system (ANFIS). The method is illustrated with a numerical example. The application of soft computing approach addresses the real situation of human judgment with fuzziness helps the managers to forecast the demand with less distortion and to improve the supply chain effectiveness.

Proceedings Article
04 Jul 2009
TL;DR: This research focuses on the adaptive course sequencing method that uses soft computing techniques as an alternative to a rule-based adaptation for an adaptive learning system and the ability of soft computing technique in handling uncertainty and incompleteness of a problem is exploited.
Abstract: Advancements in technology have led to a paradigm shift from traditional to personalized learning methods with varied implementation strategies. Presenting an optimal personalized learning path in an educational hypermedia system is one of the strategies that is important in order to increase the effectiveness of a learning session for each student. However, this task requires much effort and cost particularly in defining rules for the adaptation of learning materials. This research focuses on the adaptive course sequencing method that uses soft computing techniques as an alternative to a rule-based adaptation for an adaptive learning system. The ability of soft computing technique in handling uncertainty and incompleteness of a problem is exploited in the study. In this paper we present recent work concerning concept-based classification of learning object using artificial neural network (ANN). Self Organizing Map (SOM) and Back Propagation (BP) algorithm were employed to discover the connection between the domain concepts contained in the learning object and the learner’s learning need. The experiment result shows that this approach is assuring in determining a suitable learning object for a particular student in an adaptive and dynamic learning environment.

Proceedings ArticleDOI
02 Oct 2009
TL;DR: The main topic of the presented effort is the representation of the cause-effect relationships within medical data by the application of the soft computing technique of fuzzy cognitive maps.
Abstract: The prediction of pulmonary infections in intensive care unit is a complex medical task where a large number of parameters, tests, clinical symptoms and laboratory results are present. The knowledge of physicians according to the physical examination and clinical measurements are the main point to succeed a diagnosis and monitoring patient status. This paper presents the results of our investigation of the problem of representing knowledge for medical diagnosis systems concentrated on the pulmonary infections. The main topic of the presented effort is the representation of the cause-effect relationships within medical data by the application of the soft computing technique of fuzzy cognitive maps. The fuzzy cognitive map is a knowledge based technique for modeling and representing experts' knowledge. It can handle efficiently with complex modeling problems to assess medical decision making tasks. Due to its easy graphical representation the proposed FCM can be used to make the medical knowledge widely available through computer consultation systems.

Journal ArticleDOI
TL;DR: A comparative study of various robot motion planning schemes has been made in the present study as discussed by the authors, where two soft computing (SC)-based approaches, namely genetic-fuzzy and genetic-neural systems and a conventional potential field method (PFM) have been developed for this purpose.
Abstract: A comparative study of various robot motion planning schemes has been made in the present study. Two soft computing (SC)-based approaches, namely genetic-fuzzy and genetic-neural systems and a conventional potential field method (PFM) have been developed for this purpose. Training to the SC-based approaches is given off-line and the performance of the optimal motion planner has been tested on a real robot. Results of the SC-based motion planners have been compared between themselves and with those of the conventional PFM. Both the SC-based approaches are found to perform better than the PFM in terms of traveling time taken by the robot. Moreover, the performance of fuzzy logic-based motion planner is seen to be comparable with that of neural network-based motion planner. Comparisons among all these three motion planning schemes have been made in terms of robustness, adaptability, goal reaching capability and repeatability. Both the SC-based approaches are found to be more adaptive and robust compared to the PFM. It may be due to the fact that there is no in-built learning module in the PFM and consequently, it is unable to plan the velocity of the robot properly.

Journal Article
TL;DR: The aim of this paper was to show through a real application the flexibility and advantages of using the neuro-fuzzy network, a typical soft computing tool, for an occupational injury study.

Journal ArticleDOI
TL;DR: This study presents another soft computing approach, i.e. model trees for predicting the stability number of armor blocks, and shows that the developed models are more accurate than previous empirical and soft computing models.

BookDOI
26 Feb 2009
TL;DR: Academics, scientists as well as engineers working in the rough set, computational intelligence, soft computing and data mining research area will find the comprehensive coverage of this book invaluable.
Abstract: Along the years, rough set theory has earned a well-deserved reputation as a sound methodology for dealing with imperfect knowledge in a simple though mathematically sound way. This edited volume aims at continue stressing the benefits of applying rough sets in many real-life situations while still keeping an eye on topological aspects of the theory as well as strengthening its linkage with other soft computing paradigms. The volume comprises 11 chapters and is organized into three parts. Part 1 deals with theoretical contributions while Parts 2 and 3 focus on several real world data mining applications. Chapters authored by pioneers were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed. Academics, scientists as well as engineers working in the rough set, computational intelligence, soft computing and data mining research area will find the comprehensive coverage of this book invaluable.

Journal ArticleDOI
TL;DR: The results show the effectiveness of the proposed approach in modelling the surface roughness in end milling using adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithms (GAs) compared with other soft computing techniques.
Abstract: A study is presented to model surface roughness in end milling using adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithms (GAs). The machining parameters, namely, the spindle speed, feed rate, depth of cut and the workpiece-tool vibration amplitude have been used as inputs to model the workpiece surface roughness. The number and the parameters of membership functions used in ANFIS along with the most suitable inputs are selected using GAs maximising the modelling accuracy. The ANFIS with GAs (GA-ANFIS) are trained with a subset of the experimental data. The trained GA-ANFIS are tested using the set of validation data. The procedure is illustrated using the experimental data of a CNC vertical machining centre in end-milling of 6061 aluminum. Results are compared with other soft computing techniques like genetic programming (GP) and artificial neural network (ANN). The results show the effectiveness of the proposed approach in modelling the surface roughness.

Book
11 Mar 2009
TL;DR: The authors have consolidated their research work in this volume titled Soft Computing for Data Mining Applications, which gives an insight into the research in the fields of Data Mining in combination with Soft Computing methodologies.
Abstract: The authors have consolidated their research work in this volume titled Soft Computing for Data Mining Applications. The monograph gives an insight into the research in the fields of Data Mining in combination with Soft Computing methodologies. In these days, the data continues to grow exponentially. Much of the data is implicitly or explicitly imprecise. Database discovery seeks to discover noteworthy, unrecognized associations between the data items in the existing database. The potential of discovery comes from the realization that alternate contexts may reveal additional valuable information. The rate at which the data is stored is growing at a phenomenal rate. As a result, traditional ad hoc mixtures of statistical techniques and data management tools are no longer adequate for analyzing this vast collection of data. Several domains where large volumes of data are stored in centralized or distributed databases includes applications like in electronic commerce, bioinformatics, computer security, Web intelligence, intelligent learning database systems, finance, marketing, healthcare, telecommunications, and other fields. "With the importance of soft computing applied in data mining applications in recent years, this monograph gives a valuable research directions in the field of specialization. As the authors are well known writers in the field of Computer Science and Engineering, the book presents state of the art technology in data mining. The book is very useful to researchers in the field of data mining." N R Shetty, President, ISTE, India


Journal ArticleDOI
TL;DR: A generalist, configurable and parameterizable experimental design framework has been developed for the discovery and optimization of catalytic materials when exploring a high-dimensional space based on a soft computing architecture in which neural networks and a genetic algorithm are combined to optimize the discovery of new materials and process conditions at the industrial scale.

Journal ArticleDOI
01 Jan 2009
TL;DR: Once the GA-NN/GA-FLC system is optimized, it will be able to generate the dynamically balanced gaits of the two-legged robot in the optimal sense.
Abstract: Dynamically balanced gait generation problems of a biped robot moving up and down the sloping surface have been solved utilizing soft computing-based approaches. The gait generation problem of a biped robot is difficult to model due to its inherent complexity, imprecision in the collected data of the environment, which are the characteristics that can be the best modeled using soft computing. Two different approaches, namely genetic-neural (GA-NN) and genetic-fuzzy (GA-FLC) systems have been developed to solve the ascending and descending gait generation problems of a two-legged robot negotiating the sloping surface. Two modules of neural network (NN)/fuzzy logic controller (FLC) have been used to model the gait generation problem of a biped robot using the GA-NN/GA-FLC system. The weights of the NNs in the GA-NN and knowledge bases of the FLCs in the GA-FLC systems are optimized offline, utilizing a genetic algorithm (GA). Once the GA-NN/GA-FLC system is optimized, it will be able to generate the dynamically balanced gaits of the two-legged robot in the optimal sense.

Dissertation
01 Jan 2009
TL;DR: The statistics based Full factorial design (FFD) and the V-fold cross validation technique are applied to augment a conventional neuro-fuzzy technique and the following observations are noted: • The results obtained by applying the proposed technique are comparable and in some cases superior to those obtained by using the conventional neuroscience based fuzzy model.
Abstract: Nearly two decades back nonlinear system identification consisted of several ad-hoc approaches which were restricted to a very limited class of systems. However, with the advent of the various soft computing methodologies like neural networks, the fuzzy logic and the genetic algorithm combined with modern structure optimization techniques, a wider class of systems can be handled at present. Complex systems may be of diverse characteristics and nature. These systems may be linear or nonlinear, continuous or discrete, time varying or time invariant, static or dynamic, short term or long term, central or distributed, predictable or unpredictable, ill or well defined. System outputs may be measurable or unmeasurable. Models of real systems are of fundamental importance in virtually all disciplines and hence there is a strong demand for advanced modeling, identification and controlling schemes. This is because models help in system analysis which in turn help to get a better understanding of the system for predicting or simulating a system’s behavior. Also, system models facilitate application and validation of advanced techniques for controller design. Development of new processes and analysis of the existing ones along with their optimization, supervision, fault detection, and component diagnosis are all based on the models of the systems. As most of the real world systems are nonlinear in nature, an endeavor is made for modeling a nonlinear system in the present work. A linear system is considered to be a special case of the nonlinear system. The challenges involved in modeling, identification and control of a nonlinear system are too many and attempt has been made to tackle them by applying various soft computing methodologies. In most of the conventional soft computing methods the system modelling results are dependent on the number of training data used. It has been found that the modeling results improve as the number of training data increases. But in many complex systems the number of available training data are less and the generation of new data is also not cost effective. In such a scenario the system has to be modelled with the available data. The proposed modeling scheme has been devised keeping such a possibility in mind. The results obtained by applying this proposed model are compared with the results obtained by using various statistical and genetic algorithm based fuzzy models and finally the relative merits and demerits involved with the respective models are discussed. The work embodied in the present thesis is concerned with optimal design of the conventionally existing soft computing based system models. The statistics based Full factorial design (FFD) and the V-fold cross validation technique are applied to augment a conventional neuro-fuzzy technique and the following observations are noted : • The results obtained by applying the proposed technique are comparable and in some cases superior to those obtained by using the conventional neuro-fuzzy model. • Comparable or superior results are obtained with this proposed model even though the number of data pairs used for system modeling here are less as compared to that used in the conventional methods. • It resulted in reduction of the number of computations involved. As the experiments were performed by using reduced number of specifically chosen data, the number of computations required to be performed also came down.