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Soft computing

About: Soft computing is a research topic. Over the lifetime, 6710 publications have been published within this topic receiving 118508 citations.


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Book
01 Apr 2004
TL;DR: A Safety-Cost Based Design-Decision Support Framework Using Fuzzy Evidential Reasoning Approach and a Multi-granular Linguistic Decision Model for Evaluating the Quality of Network Services for Intelligent Sensory Evaluation Applications are presented.
Abstract: Intelligent Sensory Evaluation: An Introduction.- 1: Basic Issues.- Multidimensional Spaces of Objects and Features towards Intelligent Sensory Fusion.- A Safety-Cost Based Design-Decision Support Framework Using Fuzzy Evidential Reasoning Approach.- Using General Fuzzy Number to Handle Uncertainty and Imprecision in Group Decision-Making.- A Multi-granular Linguistic Decision Model for Evaluating the Quality of Network Services.- From Measurements to Validation - Soft Computing Contributions.- Mechanism of Trust in Panel System.- Evaluating Schedule Performance in Flexible Job-Shops.- 2: Sensory Evaluation Applications.- Formalization of at-line Human Evaluations to Monitor Product Changes during Processing: The Concept of Sensory Indicator.- The Fuzzy Symbolic Approach for the Control of Sensory Properties in Food Processes.- Fuzzy Inference Systems to Model Sensory Evaluation.- A 2-Tuple Fuzzy Linguistic Model for Sensory Fabric Hand Evaluation.- Sensory Evaluation Driven Methodology for Measurement System Design.- Man-Machine Interaction to Extract Features of Odorous Molecules.- Computerized Evaluation of Visual Capability Using Fuzzy Logic and ROC Theory.- Measuring Software Development Value Using Fuzzy Logic.- 3: Related Industrial Applications.- Planning for Sustainability in the Belgian Electricity Sector: A Multi-criteria Mapping Exercise.- Adaptive Modeling and Control of Drug Delivery Systems Using Generalized Fuzzy Neural Networks.- Classification of Breast Cancers Using Dynamic Fuzzy Neural Networks.- Face Recognition Using an RBF Neural Classifier with Hybrid Learning.- Application of Fuzzy-Integration-Based Multiple-Information Aggregation in Automatic Speech Recognition.- Automated Quality Control in Sound Speakers Manufacturing Using a Hybrid Neuro-Fuzzy-Fractal Approach.- Hardware Implementation of a Fuzzy Controller for the Battery Charging Process in an Industrial Plant.

36 citations

Journal ArticleDOI
Harish Garg1
TL;DR: In this article, the authors addressed the various reliability parameters of the industrial system, which depicts the behavior of the system, by quantifying the uncertainties in the data in the form of fuzzy numbers.
Abstract: Due to imprecise information, it is always difficult for the system analyst to predict and enhance the performance of the system up to the desired degree of accuracy. Therefore, the main task is to reduce the uncertainty level for decision makers, so as to take a more sound decision in a reasonable time. For handling these issues, this paper addressed the various reliability parameters of the industrial system, which depicts the behavior of the system, by quantifying the uncertainties in the data in the form of fuzzy numbers. The corresponding membership functions of the system’s parameters are computed by formulating a nonlinear optimization model and solve it. The obtained results were compared with the existing as well as traditional methodology and results and found that they had less range of uncertainties during the analysis. A sensitivity as well as performance analysis has also been done for depicting the most critical component of the system. Finally, an approach has been illustrated through a case study of cattle feed plant, a repairable industrial system.

36 citations

Book ChapterDOI
15 Dec 2005
TL;DR: In this article, a new feature selection approach named Correlation-based Hybrid Feature Selection (CBHFS) is proposed to model lightweight intrusion detection system (IDS) which is able to significantly decrease training and testing times while retaining high detection rates with low false positives rates as well as stable feature selection results.
Abstract: Modeling IDS have been focused on improving detection model(s) in terms of (i) detection model design based on classification algorithm, clustering algorithm, and soft computing techniques such as Artificial Neural Networks (ANN), Hidden Markov Model (HMM), Support Vector Machines (SVM), K-means clustering, Fuzzy approaches and so on and (ii) feature selection through wrapper and filter approaches. However these approaches require large overhead due to heavy computations for both feature selection and cross validation method to minimize generalization errors. In addition selected feature set varies according to detection model so that they are inefficient for modeling lightweight IDS. Therefore this paper proposes a new approach to model lightweight Intrusion Detection System (IDS) based on a new feature selection approach named Correlation-based Hybrid Feature Selection (CBHFS) which is able to significantly decrease training and testing times while retaining high detection rates with low false positives rates as well as stable feature selection results. The experimental results on KDD 1999 intrusion detection datasets show the feasibility of our approach to enable one to modeling lightweight IDS.

36 citations

Journal ArticleDOI
TL;DR: The Response Surface Method (RSM) is improved based on high-order polynomial functions for forecasting the river stream-flow namely; High-Order Response Surface (HORS) method, which showed outstanding performance for monthly stream- flow forecasting at AHD.
Abstract: Accurate and reliable stream-flow forecasting has a key role in water resources planning and management. Most recently, soft computing approaches have become progressively prevalent in modelling hydrological variables and most specifically stream-flows. This is due to their ability to capture the non-linearity and non-stationarity characteristics of the hydrological variables with minimum information requirements. Despite this, they present several challenges in the modelling architecture, as there is a need to establish a suitable pre-processing method for the stream-flow data and an appropriate optimization model has to be integrated in order re-adjust the weights and biases associated with the model structure. On top of that, artificial intelligent models require “trial and error” procedures in order to be properly tuned (number of hidden layers, number of neurons within the hidden layers and the type of the transfer function). However, soft computing approach experienced several problems while calibration such as over-fitting. In this research, the Response Surface Method (RSM) is improved based on high-order polynomial functions for forecasting the river stream-flow namely; High-Order Response Surface (HORS) method. Several higher orders have been examined, second, third, fourth and fifth polynomial functions in order to figure out the best fit that able to mimic the pattern of stream-flow. In order to demonstrate the effectiveness of the proposed model, monthly stream-flow time series data located in Aswan High Dam (AHD) has been examined. A detailed analysis of the overall statistical indicators revealed that the proposed method showed outstanding performance for monthly stream-flow forecasting at AHD. It could be concluded that the fifth order polynomial function outperforms the other orders of the polynomial functions especially with May model who achieved minimum MAE 0.12, NRMSE 0.07, MSE 0.03 and maximum SF and R2 (0.97, 0.99) respectively.

36 citations

Journal ArticleDOI
TL;DR: Bayesian neural networks model is chosen and successfully improved by applying the Bayesian inference at four hierarchical levels: for training, optimization of the regularization terms, data‐based model selection, and evaluation of the relative importance of different inputs.
Abstract: In recent years, structural integrity monitor- ing has become increasingly important in structural en- gineering and construction management. It represents an important tool for the assessment of the dependability of existing complex structural systems as it integrates, in a unified perspective, advanced engineering analyses and experimental data processing. In thefirst part of this work the concepts of dependability and structural integrity are discussed and it is shown that an effective integrity assess- ment needs advanced computational methods. For this purpose, soft computing methods have shown to be very useful. In particular, in this work the neural networks model is chosen and successfully improved by apply- ing the Bayesian inference at four hierarchical levels: for training, optimization of the regularization terms, data- based model selection, and evaluation of the relative im- portance of different inputs. In the second part of the ar- ticle, Bayesian neural networks are used to formulate a multilevel strategy for the monitoring of the integrity of long span bridges subjected to environmental actions: in a first level the occurrence of damage is detected; in a fol- lowing level the specific damaged element is recognized and the intensity of damage is quantified.

36 citations


Network Information
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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023159
2022270
2021319
2020332
2019313
2018348