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Thematische Bibliographien / SOFT COMPUTING TECHNIQUE / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „SOFT COMPUTING TECHNIQUE“

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Veröffentlicht am 11. September 2023

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1

Yu, Yingbing. „Anomaly Intrusion Detection Based upon Anomalous Events and Soft Computing Technique“. International Journal of Machine Learning and Computing 5, Nr. 6 (Dezember 2015): 450–53. http://dx.doi.org/10.18178/ijmlc.2015.5.6.550.

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Sabri, Norlina M., Mazidah Puteh und Mohamad Rusop Mahmood. „Utilization of Soft Computing Techniques in Sputtering Processes: A Review“. Advanced Materials Research 832 (November 2013): 260–65. http://dx.doi.org/10.4028/www.scientific.net/amr.832.260.

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This paper presents an overview of research works on the utilizing of soft computing in the optimization of process parameters and in the prediction of thin film properties in sputtering processes. The papers from this review were obtained from relevant databases and from various scientific journals. The papers collected were published from 2008 to 2012. The focus of the review is to provide an outlook on the utilization of soft computing techniques in sputtering processes. Based on the review, the soft computing techniques which have been applied so far are ANN, GA and Fuzzy Logic. The first finding of this review is that soft computing technique is a promising and more reliable approach to optimize and predict process parameters compared to the traditional methods. The second finding is that the utilizing of soft computing techniques in sputtering processes are still limited and still in exploratory phase as they have not yet been extensively and stably applied. The techniques applied are also limited to ANN, GA and Fuzzy, whereas the exploration into other techniques is also necessary to be conducted in order to seek the most reliable technique and so as to expand the application of soft computing approach. Future research could focus on the exploration of other soft computing techniques for optimization in order to find the best optimization techniques based on the specific processes.

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Wong, K. W., P. M. Wong, T. D. Gedeon und C. C. Fung. „Rainfall prediction model using soft computing technique“. Soft Computing - A Fusion of Foundations, Methodologies and Applications 7, Nr. 6 (01.05.2003): 434–38. http://dx.doi.org/10.1007/s00500-002-0232-4.

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4

Kaur, Supreet, und Rajiv Mahajan. „Energy efficient clustering protocol for wireless sensor networks“. Modern Physics Letters B 32, Nr. 32 (20.11.2018): 1850400. http://dx.doi.org/10.1142/s0217984918504006.

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In this paper, a hybrid soft computing technique-based energy efficient protocol is proposed to improve the inter-cluster data aggregation in clustering based general self-organized tree based energy balance (GSTEB) routing protocol. Initially, improved ant colony optimization-based technique is used to select optimal cluster heads. Afterwards, a hybrid soft computing technique is utilized to communicate the data from cluster heads to sink. Extensive experiments have been done by considering the existing and proposed technique. Experimental results indicate that the proposed technique provides better network lifetime as compared to existing techniques.

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Singh, Dharmpal. „An Effort to Design an Integrated System to Extract Information Under the Domain of Metaheuristics“. International Journal of Applied Evolutionary Computation 8, Nr. 3 (Juli 2017): 13–52. http://dx.doi.org/10.4018/ijaec.2017070102.

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The main objective of this work is to develop an integrated system that is capable of extracting precise information (knowledge) based on any stored information using the techniques of data mining and soft computing. For the purpose of extracting precise information based on some stored information, it has been further observed that the research work related to the area of knowledge discovery based on certain information with the help of a particular data mining or soft computing model has been done, but the performance based on the particular soft computing or data mining model has not been reviewed as compared to the other models. The comparison of performance of various models in the area of soft computing domain or statistical domain or data mining area have been remained unattended with limitation of the survey. This absence leads to the necessity and carrying out research work for effective knowledge discovery based on a particular set of information on utilizing the versatility and potential view generation soft computing tools. The modified harmony search technique has been proposed in this paper and it has been observed that it has outperformed the other soft computing technique in case of training and tested data. The result of the modified harmony search technique has also been cross checked by the residual error. The concept of harmony search is also applied to other data set to check the optimality of the models.

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Sesadri, U., B. Siva Sankar und C. Nagaraju. „Type2 Fuzzy Soft Computing Technique for Image Enhancement“. IAES International Journal of Artificial Intelligence (IJ-AI) 4, Nr. 3 (01.09.2015): 97. http://dx.doi.org/10.11591/ijai.v4.i3.pp97-104.

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<p class="Default">The mainpurpose of Image enhancement is to process an image so that outcome is more appropriate than original image for definite application. The fuzzy logic isone of the soft computing techniques to enhance the images by eliminating uncertainty.In this paper efficient type2 fuzzy logic technique is used to get betterquality image. This method consists of two steps. In the First step fisher criterion function is useful to generate type1 fuzzy membership value. In the second step based on type1 membership value fuzzy rules are derived to enhance the image. The type2 fuzzy method is compared with type1 fuzzy. The table values and graphs provethat the proposed method gives better results compared with fuzzy type1 method.</p>

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KumarSaini, Dinesh, und Jabar H Yousif. „Environmental Scrutinizing System based on Soft Computing Technique“. International Journal of Computer Applications 62, Nr. 13 (18.01.2013): 45–50. http://dx.doi.org/10.5120/10143-4952.

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Manh, Linh Ho, Francesco Grimaccia, Marco Mussetta und Riccardo E. Zich. „A Soft Computing Hybridization Technique for Antenna Optimization“. International Journal on Communications Antenna and Propagation (IRECAP) 5, Nr. 1 (28.02.2015): 16. http://dx.doi.org/10.15866/irecap.v5i1.4899.

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Sudipta Sen, Sudipta Sen. „Hydro-Thermal Scheduling: Using Soft Computing Technique Approch“. IOSR Journal of Electrical and Electronics Engineering 5, Nr. 6 (2013): 55–80. http://dx.doi.org/10.9790/1676-0565580.

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10

Ghadiyali, Tejaskumar, und Kalpesh Lad. „Risk Minimization in Agribusiness using Soft Computing Technique“. IOSR Journal of Computer Engineering 18, Nr. 05 (Mai 2016): 20–25. http://dx.doi.org/10.9790/0661-1805042025.

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11

Das, Ratnakar, Jibitesh Mishra, Pradyumna Kumar Pattnaik und Muhammad Mubashir Bhatti. „Prediction of Heatwave Using Advanced Soft Computing Technique“. Information 14, Nr. 8 (07.08.2023): 447. http://dx.doi.org/10.3390/info14080447.

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At present, there is no suitable instrument available to simulate modeling the thermal performance of various areas of our states due to its complicated meteorological behavior. To accurately predict a heatwave, we studied the research gaps and current ongoing research on the prediction of heatwaves. For the accurate prediction of a heatwave, we considered two soft computing concepts, (a) Rough Set Theory (RST) and (b) Support Vector Machine (SVM). All the ongoing research on the prediction of heatwaves is based on future predictions with an error margin. All the available techniques use a particular pattern of heatwave data, and these methods do not apply to vague data. This paper used an innovative RST and SVM technique, which can be applied to vague and imprecise datasets to produce the best outcomes. RST is helpful in finding the most significant attributes that will be alarming in the future. This analysis identifies the heat wave as the most prominent characteristic among various meteorological data. SVM is responsible for the future prediction of heat waves, which includes various parameters. By further classification of heatwaves, we found that a lack of greenery will increase the heatwave in the future. Although the survey was conducted based on a sampling distribution, we expect this result to represent the population as we collected our sample in a heterogeneous environment. These outcomes are validated using a statistical method.

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Shen, Zhongjie, Ahmed Farouk Deifalla, Paweł Kamiński und Artur Dyczko. „Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning“. Materials 15, Nr. 10 (13.05.2022): 3523. http://dx.doi.org/10.3390/ma15103523.

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In civil engineering, ultra-high-strength concrete (UHSC) is a useful and efficient building material. To save money and time in the construction sector, soft computing approaches have been used to estimate concrete properties. As a result, the current work used sophisticated soft computing techniques to estimate the compressive strength of UHSC. In this study, XGBoost, AdaBoost, and Bagging were the employed soft computing techniques. The variables taken into account included cement content, fly ash, silica fume and silicate content, sand and water content, superplasticizer content, steel fiber, steel fiber aspect ratio, and curing time. The algorithm performance was evaluated using statistical metrics, such as the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The model’s performance was then evaluated statistically. The XGBoost soft computing technique, with a higher R2 (0.90) and low errors, was more accurate than the other algorithms, which had a lower R2. The compressive strength of UHSC can be predicted using the XGBoost soft computing technique. The SHapley Additive exPlanations (SHAP) analysis showed that curing time had the highest positive influence on UHSC compressive strength. Thus, scholars will be able to quickly and effectively determine the compressive strength of UHSC using this study’s findings.

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Sindhwani, Manoj, Shippu Sachdeva, Krishan Arora, Taehyun Yoon, Daeseung Yoo, Gyanendra Prasad Joshi und Woong Cho. „Soft Computing Techniques Aware Clustering-Based Routing Protocols in Vehicular Ad Hoc Networks: A Review“. Applied Sciences 12, Nr. 15 (07.08.2022): 7922. http://dx.doi.org/10.3390/app12157922.

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The vehicular ad hoc network is an emerging area of technology that provides intelligent transportation systems with vast advantages and applications. Frequent disconnections between the vehicular nodes due to high-velocity vehicles impact network performance. This can be addressed by efficient clustering techniques. Several recent studies have attempted to develop optimal clustering algorithms to improve network performance metrics using soft computing techniques. Although sufficient work on soft computing techniques has been carried out, it seems less commonplace to find an analysis of various algorithms’ network parameters together. This paper provides a systematic analysis of the clustering-based routing protocols used in vehicular networks that are aware of soft computing techniques. The categorization is performed according to various soft computing techniques: particle swarm optimization, k-means, neural networks, artificial bee colony, genetic algorithm, firefly algorithm, and fuzzy logic. A comparative study of soft computing strategies is also provided in the survey with a focus on their objectives, along with their strengths and limitations. This survey makes it easier for researchers to pick the required soft computing technique used in vehicular networks in order to improve metrics such as packet delivery ratio, end-to-end delay, throughput, cluster lifetime, and message overhead.

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A., Dr Sathesh. „ENHANCED SOFT COMPUTING APPROACHES FOR INTRUSION DETECTION SCHEMES IN SOCIAL MEDIA NETWORKS“. Journal of Soft Computing Paradigm 2019, Nr. 2 (09.12.2019): 69–79. http://dx.doi.org/10.36548/jscp.2019.2.002.

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The soft computing methods play a vital role in identifying the malicious activities in the social network. The low cost solutions and the robustness provided by the soft computing in the identifying the unwanted activities make it a predominant area of research. The paper combines the soft computing techniques and frames an enhanced soft computing approach to detect the intrusion that cause security issues in the social network. The proffered method of the paper employs the enhanced soft computing technique that combines the fuzzy logic, decision tree, K means -EM and the machine learning in preprocessing, feature reduction, clustering and classification respectively to develop a security approach that is more effective than the traditional computations in identifying the misuse in the social networks. The intrusion detection system developed using the soft computing approach is tested using the KDD-NSL and the DARPA dataset to note down the security percentage, time utilization, cost and compared with the other traditional methods.

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S. Kumar Chandar, Dr. „Soft computing and bioinspired computing techniques for stock market prediction-a comprehensive survey“. International Journal of Engineering & Technology 7, Nr. 3 (22.08.2018): 1836. http://dx.doi.org/10.14419/ijet.v7i3.14716.

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Stock Market Prediction (SMP) is one of the most important and hottest topics in business and finance. The main goal of SMP is to develop an efficient technique to predict stock values and achieves accurate results with minimum number of input data. This research paper reviews currently available SMP techniques based on soft computing and bio inspired computing algorithms. Many issues in-volved in the SMP are identified and different techniques are studied along with their merits and demerits to find the most suitable one. This paper also analyses the performance of various techniques with respect to some metrics including MSE, RMSE, MAD, MAPE, AAE and Hit ratio. The reviewed papers are classified in terms of number of input variables, prediction method and evaluation parame-ters used. A tabular representation of all the SMP techniques is presented to facilitate the future comparison. From the reviewed paper, it is noticed that the integration of soft computing with the bio inspired algorithms has the potential to predict the stock market index with high accuracy and achieves best result than soft computing method alone.

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Yadav, Yogesh, und Ishwor Singh Saud. „Development of a Model Updating Technique Using Soft Computing“. Journal of Advanced College of Engineering and Management 7, Nr. 01 (25.08.2022): 183–90. http://dx.doi.org/10.3126/jacem.v7i01.47344.

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The structural models, do not necessarily predict the measured data sufficiently accurately. Because of this, there is a need for these models to be updated to better reflect the measured data. This paper introduces computational intelligence techniques to update finite element models. The model updating is formulated as a constrained optimization problem and solved using a recently developed meta-heuristic algorithm called Artificial Bee Colony (ABC) algorithm. A MATLAB code is developed using the model-updating formulations presented in this paper. Numerical simulation studies are carried out by solving for the proposed model updating technique by using pseudo-experimental data of an 8storey framed structure and also the experimental data of ASCE three storey benchmark structure. Studies presented in this paper clearly indicate the effectiveness of the proposed computational intelligence-based model updating technique.

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Mathur, Ashwani. „Image Retrieval by Soft Computing Technique and Visual Features“. International Journal of Computer Applications 174, Nr. 16 (15.01.2021): 8–12. http://dx.doi.org/10.5120/ijca2021921047.

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18

Vijayeeta, Prachi, Utsav Kar, Madhurima Rana, Madhabananda Das und B. S. P. Mishra. „Microarray Classification of Cancerous Cell Using Soft Computing Technique“. Procedia Computer Science 49 (2015): 66–73. http://dx.doi.org/10.1016/j.procs.2015.04.228.

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19

Vyas, Vidhi, Ajit Pratap Singh und Anshuman Srivastava. „Quantification of airfield pavement condition using soft-computing technique“. World Journal of Engineering 17, Nr. 6 (07.10.2020): 877–90. http://dx.doi.org/10.1108/wje-01-2020-0021.

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Purpose The purpose of this study is the development of an objective approach to prioritize and rank airfield pavement sections based on their condition and justify their funding requirements using a soft-computing technique. Design/methodology/approach The airfield pavement condition is evaluated by collecting data through field tests and visual surveys. The performance indicators are selected as deflection, structural index, subgrade modulus and pavement condition index, by taking the help of field experts. The condition of pavement sections is analyzed by obtaining scores for each sections using Buckley’s fuzzy analytic hierarchy process. The sections are finally ranked for performing their maintenance and repair activities. Findings The condition of pavements is represented using a single score that takes an account of cumulative impact of various parameters as well as any subjectivity associated with human perceptions. Practical implications The developed methodology is very useful for its practical implications, and it is explained using a case study of an international airport. Originality/value Decision-making for maintenance and repair practices is often based on subjective decisions and lacks a robust and judicious approach. Thus, obtaining sufficient budget for repair and maintenance becomes one of the primary challenges. This study adds a value to prevailing practices by developing an objective decision-making methodology. Additionally, the use of non-destructive testing techniques, which pose little or no necessity to destructive coring and boring, eases this task.

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Deshpande, Ms Pooja S., und Prof S. J. Honade. „Brain Tumor Segmentation and Detection using Soft Computing Technique“. IOSR Journal of Electronics and Communication Engineering 12, Nr. 02 (Mai 2017): 129–44. http://dx.doi.org/10.9790/2834-120203129144.

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Issa, Abbas H., und Ali H. Majeed. „Intelligent sensor fault detection based on soft computing“. Kufa Journal of Engineering 4, Nr. 1 (30.01.2014): 113–24. http://dx.doi.org/10.30572/2018/kje/411246.

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Sensor fault detection is carried out based on the characteristics of the soft computing techniques; neural network and adaptive neural fuzzy inference system ANFIS. In this paper, a neural network (non-model based technique) and ANFIS has been used for detection and isolation of temperature sensor fault TMP36. The measured states are then compared with true estimated states and if their difference exceeds threshold value, the particular sensor measurement is ignored and replaced by the true estimated state. Residual generation is an essential part of model-based fault detection schemes. This paper develops and implements neural-network and ANFIS based system identification techniques for nonlinear systems with the specific goal of residual generation for fault detection purposes. The two approaches are tested on a temperature sensor model. Performance comparisons of the two neural network and ANFIS are presented.

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LV, Yingli, Qui-Thao Le, Hoang-Bac Bui, Xuan-Nam Bui, Hoang Nguyen, Trung Nguyen-Thoi, Jie Dou und Xuan Song. „A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer“. Applied Sciences 10, Nr. 2 (16.01.2020): 635. http://dx.doi.org/10.3390/app10020635.

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In this study, the ilmenite content in beach placer sand was estimated using seven soft computing techniques, namely random forest (RF), artificial neural network (ANN), k-nearest neighbors (kNN), cubist, support vector machine (SVM), stochastic gradient boosting (SGB), and classification and regression tree (CART). The 405 beach placer borehole samples were collected from Southern Suoi Nhum deposit, Binh Thuan province, Vietnam, to test the feasibility of these soft computing techniques in estimating ilmenite content. Heavy mineral analysis indicated that valuable minerals in the placer sand are zircon, ilmenite, leucoxene, rutile, anatase, and monazite. In this study, five materials, namely rutile, anatase, leucoxene, zircon, and monazite, were used as the input variables to estimate ilmenite content based on the above mentioned soft computing models. Of the whole dataset, 325 samples were used to build the regarded soft computing models; 80 remaining samples were used for the models’ verification. Root-mean-squared error (RMSE), determination coefficient (R2), a simple ranking method, and residuals analysis technique were used as the statistical criteria for assessing the model performances. The numerical experiments revealed that soft computing techniques are capable of estimating the content of ilmenite with high accuracy. The residuals analysis also indicated that the SGB model was the most suitable for determining the ilmenite content in the context of this research.

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Chanda, Mriganka Mohan, Neelotpaul Banerjee und Gautam Bandyopadhyay. „Effective Implementation of Knowledge Management Systems (KMS) in Government Schemes/Programs of Selected Sectors Using Soft Computing“. International Journal of Knowledge Management 18, Nr. 1 (Januar 2022): 1–22. http://dx.doi.org/10.4018/ijkm.297608.

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Soft Computing (SC) technique consisting of several fields of Artificial Intelligence namely, Artificial Neural Networks (ANNs), Fuzzy Logic (FL), Genetic Algorithms (GA), Machine Learning (ML) and Probabilistic Reasoning (PR) is a new paradigm in computing based on a creative mix of comparatively newer computing techniques. Here, we have taken into consideration several important schemes/ programs of selected sectors pertaining to different Central Ministries/ Departments of Government of India and observed that proper Knowledge Management System (KMS) can be developed and implemented in such cases using ANN based soft computing input output model as an effective tool for the same. Further, we have analyzed how various input and output parameters (both physical and financial) associated with each such selected scheme/ program are related to each other in the light of their basic objectives and the way these can be appropriately represented by an ANN based soft computing input output model in which the expected outputs corresponding to certain inputs can be calculated/ estimated.

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Nadaradjane, S. M. R., B. Priyenga und N. Sanjay. „Clinical level diagnosis of chagas disease using soft computing technique“. International Journal of Infectious Diseases 101 (Dezember 2020): 433–34. http://dx.doi.org/10.1016/j.ijid.2020.09.1136.

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S.P.shukla, Mr, und Mrs Ritu Dwivedi. „Clustering and Classification of Cancer Data Using Soft Computing Technique“. IOSR Journal of Computer Engineering 16, Nr. 1 (2014): 32–36. http://dx.doi.org/10.9790/0661-16113236.

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Abed, Wathiq, Qais Aish und Ahmmed Hamed. „Performance monitoring of aircraft PMSM based on soft computing technique“. Periodicals of Engineering and Natural Sciences (PEN) 9, Nr. 1 (22.02.2021): 293. http://dx.doi.org/10.21533/pen.v9i1.1797.

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Srujan kumar, Gudiboyana, Korra Mohan rao und N. Praneeth. „A Simple and Efficient Soft Computing Technique for Optimizing Mechatronics“. International Journal of Engineering Trends and Technology 22, Nr. 9 (25.04.2015): 443–46. http://dx.doi.org/10.14445/22315381/ijett-v22p290.

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Idris, Sitinoor Adeib, und Masturah Markom. „Overview of soft intelligent computing technique for supercritical fluid extraction“. International Journal of Advances in Applied Sciences 9, Nr. 2 (01.06.2020): 117. http://dx.doi.org/10.11591/ijaas.v9.i2.pp117-124.

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<span>Optimization of Supercritical Fluid Extraction process with mathematical modeling is essential for industrial applications. The response surface methodology (RSM) has been proven to be a useful and effective statistical method for studying the relationships between measured responses and independent factors. Recently there are growing interest in applying smart system or artificial technique to model and simulate a chemical process and also to predict, compute, classify and optimize as well as for process control. This system works by generalizing the experimental result and the process behavior and finally predict and estimate the problem. This smart system is a major assistance in the development of process from laboratory to pilot or industrial. The main advantage of intelligent systems is that the predictions can be performed easily, fast, and accurate way, which physical models unable to do. This paper shares several works that have been utilizing intelligent systems for modeling and simulating the supercritical fluid extraction process.</span>

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Samira, Kalantari, Amiri besheli Mohsen, Sam Daliri Zeinab, Shamshirb Shahaboddin und Shing Ng Liang. „Routing in wireless sensor network based on soft computing technique“. Scientific Research and Essays 6, Nr. 21 (30.09.2011): 432–4441. http://dx.doi.org/10.5897/sre10.306.

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Das, Gourab. „Techno-economic analysis of novel multi-objective soft computing technique“. International Journal of Bio-Inspired Computation 20, Nr. 3 (2022): 172. http://dx.doi.org/10.1504/ijbic.2022.10052581.

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Das, Gourab. „Techno-economic analysis of novel multi-objective soft computing technique“. International Journal of Bio-Inspired Computation 20, Nr. 3 (2022): 172. http://dx.doi.org/10.1504/ijbic.2022.127505.

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Maddineni, Bhavyasri. „Analysis of Beam Forming Antenna using Soft-Computing Techniques“. International Journal for Research in Applied Science and Engineering Technology 9, Nr. VI (14.06.2021): 661–65. http://dx.doi.org/10.22214/ijraset.2021.35050.

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Antenna Beam forming is a technology or a technique that is finding increasing use in systems of cellular telecommunication, especially 5G, as well as many other wireless systems. Beam forming refers to the formation of a beam of energy from a set of phased arrays. With the use of phased arrays, it is possible to control the direction and shape of the beam from multiple antennas, based on the spacing between antennas and the phase of signal from each antenna element in the array. Beam forming focuses a wireless signal towards a specific receiving device, rather than having the signal spread in all directions from the broadcast antenna. By focusing signal in specific direction, the beam forming technique allows delivery of higher signal quality to the receiver, which would result in faster information transfer with fewer errors and without the need to boost the broadcast power. Beam forming technique is also used to reduce the interference of signals. Beam forming is majorly involved with computing resources which requires high time and power resources. Parameters that would be analyzed during this project are Half Power Beam width (HPBW), First Null Beam width (FNBW), Gain, Voltage Standing Wave Ratio, Front to Back Power Ratio, Side Lobe Levels.

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Prashanth, R., und D. S. Nimaje. „Estimation of peak particle velocity using soft computing technique approaches: a review“. Noise & Vibration Worldwide 49, Nr. 9-10 (24.09.2018): 302–10. http://dx.doi.org/10.1177/0957456518799536.

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Ground vibrations caused by blasting are undesirable consequences in the mining industry. It can cause serious damage to the nearby buildings and facilities. Hence, such vibrations have to be controlled to reduce the damage to the environment, and this may be achieved by predicting the blast peak particle velocity. The induced peak particle velocity has influenced by a number of parameters. They are spacing, burden, distance from the blast site, maximum explosive charge per delay, number of holes, stemming, and hole diameter. Existed empirical predictor approaches were used to evaluate the ground vibrations based on two parameters: distance and maximum explosive charge per hole only. To overcome the limitations of empirical methods, soft computing techniques are employed to estimate the peak particle velocity accurately. Since the late 1990s, several soft computing techniques such as artificial neural networks, fuzzy logic, and genetic algorithms have been proposed for achieving accurate prediction. This article presents a summary review of the developed soft computing in the 10 years following 2006 with particular emphasis on the number of influenced parameters and coefficient of determination ( R2).

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Ilango, S. Sudhakar, und V. Seenivasagam. „Wavelet Based Image Compression Using Soft Computing Techniques“. Applied Mechanics and Materials 573 (Juni 2014): 477–82. http://dx.doi.org/10.4028/www.scientific.net/amm.573.477.

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The Wavelet Transform is a multi-resolution transform, that is, it allows a form of time–frequency analysis (or translation–scale in wavelet speak). When using the Fourier transform the result is a very precise analysis of the frequencies contained in the signal, but no information on when those frequencies occurred. The wavelet based image compression algorithms are used widely compared with other conventional compression algorithms. The wavelet coding based on the coefficient selection and sub band level. In this paper we have used two wavelets such as spherical and geometric wavelets. The spherical representation is a hierarchical description of how total energy gets distributed within each wavelet sub band.In the proposed method, we used fuzzy quantization technique for coefficient selection in the spherical wavelet. The other scheme introduces binary space partitioning scheme and geometric wavelet, where the existing pruning method of binary space partitioning is replaced by the genetic algorithm. We had another experiment with geometric wavelet with Artificial Bee Colony (ABC) algorithm. The experimental results for all the three methods are discussed in this paper. The advantages of these methods are the improved PSNR values at high and medium bit rates.

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Crawford, Broderick, Ricardo Soto, Eric Monfroy, Carlos Castro, Wenceslao Palma und Fernando Paredes. „A Hybrid Soft Computing Approach for Subset Problems“. Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/716069.

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Subset problems (set partitioning, packing, and covering) are formal models for many practical optimization problems. A set partitioning problem determines how the items in one set (S) can be partitioned into smaller subsets. All items inSmust be contained in one and only one partition. Related problems are set packing (all items must be contained in zero or one partitions) and set covering (all items must be contained in at least one partition). Here, we present a hybrid solver based on ant colony optimization (ACO) combined with arc consistency for solving this kind of problems. ACO is a swarm intelligence metaheuristic inspired on ants behavior when they search for food. It allows to solve complex combinatorial problems for which traditional mathematical techniques may fail. By other side, in constraint programming, the solving process of Constraint Satisfaction Problems can dramatically reduce the search space by means of arc consistency enforcing constraint consistencies either prior to or during search. Our hybrid approach was tested with set covering and set partitioning dataset benchmarks. It was observed that the performance of ACO had been improved embedding this filtering technique in its constructive phase.

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Govind, Annu, Vijay Kumar Tayal und Prakash Kumar. „Performance enhancement of shunt active power filter using soft computing techniques“. International Journal of Applied Power Engineering (IJAPE) 10, Nr. 3 (01.09.2021): 253. http://dx.doi.org/10.11591/ijape.v10.i3.pp253-261.

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Nowadays, active power filter (APF) is the most popular device for harmonic compensation. This paper presents the soft computing techniques for compensation of currents harmonics using a shunt active power filter (SAPF). The method includes a 3-phase supply system with a current-controlled voltage source converter (CC-VSC) having an input coupling inductor and output tank capacitor for a self-supported DC bus. The performance of the active power filter can be enhanced by using soft computing techniques such as artificial neural network (ANN) controller and gravitational search algorithm (GSA) for generating control signals of the SAPF. The current reference is calculated to compensate source current THD with synchronous reference frame (SRF) technique with proportional integrator (PI) controller. From the result, it is evident that both the soft computing techniques reduce the computational time & fast convergence which improves the filter performance during the transient period and makes it self-tuned. The proposed structure is simulated using MATLAB/Simulink and subsequently experimentally verified. The presentation of the system is originated to be suitable for numerous features of power quality enhancement structure.

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Tanaka, Kazuo. „Advanced Computational Intelligence in Control Theory and Applications“. Journal of Advanced Computational Intelligence and Intelligent Informatics 3, Nr. 2 (20.04.1999): 67. http://dx.doi.org/10.20965/jaciii.1999.p0067.

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We are witnessing a rapidly growing interest in the field of advanced computational intelligence, a "soft computing" technique. As Prof. Zadeh has stated, soft computing integrates fuzzy logic, neural networks, evolutionary computation, and chaos. Soft computing is the most important technology available for designing intelligent systems and control. The difficulties of fuzzy logic involve acquiring knowledge from experts and finding knowledge for unknown tasks. This is related to design problems in constructing fuzzy rules. Neural networks and genetic algorithms are attracting attention for their potential in raising the efficiency of knowledge finding and acquisition. Combining the technologies of fuzzy logic and neural networks and genetic algorithms, i.e., soft computing techniques will have a tremendous impact on the fields of intelligent systems and control design. To explain the apparent success of soft computing, we must determine the basic capabilities of different soft computing frameworks. Give the great amount of research being done in these fields, this issue addresses fundamental capabilities. This special issue is devoted to advancing computational intelligence in control theory and applications. It contains nine excellent papers dealing with advanced computational intelligence in control theory and applications such as fuzzy control and stability, mobile robot control, neural networks, gymnastic bar action, petroleum plant control, genetic programming, Petri net, and modeling and prediction of complex systems. As editor of this special issue, I believe that the excellent research results it contains provide the basis for leadership in coming research on advanced computational intelligence in control theory and applications.

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Kumaraswamy, Durgam, und B. V. Sanker Ram. „Fuel Cell Power Conversion Enhancement using Fuzzy Based Soft Computing Technique“. Indian Journal of Science and Technology 10, Nr. 23 (01.02.2017): 1–11. http://dx.doi.org/10.17485/ijst/2017/v10i23/101347.

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Zhang, Hong Min. „Path Planning Methods of Mobile Robot Based on Soft Computing Technique“. Advanced Materials Research 216 (März 2011): 677–80. http://dx.doi.org/10.4028/www.scientific.net/amr.216.677.

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Path planning is one of the most important and challenging problems of mobile robot. It is one of the keys that will make the mobile robots fully autonomous. In this paper, we summarized the application of soft computing approaches in path planning for mobile robot. Finally the future works of path planning for mobile robots are prospected.

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Dhanasekar, N., und R. Kayalvizhi. „Soft Computing Technique for the Control of Triple-Lift Luo Converter“. International Journal of Engineering Research and Applications 07, Nr. 03 (März 2017): 35–38. http://dx.doi.org/10.9790/9622-0703063538.

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Pallekonda, Ramesh Babu, Soumya Ranjan Nanda, Santosha K. Dwivedy, Vinayak Kulkarni und Viren Menezes. „Soft Computing Based Force Recovery Technique for Hypersonic Shock Tunnel Tests“. International Journal of Structural Stability and Dynamics 18, Nr. 05 (Mai 2018): 1871004. http://dx.doi.org/10.1142/s0219455418710049.

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A hemispherical model equipped with a three component accelerometer force balance has been tested in a shock tunnel at Mach 8.0 freestream conditions. A novel technique has been devised using the Artificial Neuro-Fuzzy Inference System (ANFIS) for recovering the forces experienced by the model during the experiments. Implementation of this methodology in calibration of the force balance showed encouraging agreement with the impulse forces recovered from the calibration tests. The same recovery procedure is then adopted to obtain the time history of the forces for 0[Formula: see text] and 15[Formula: see text] angle of attack experiments. The drag recovered in steady state is found to agree well with the conventional methods with minor discrimination for the lift and pitching moment. In light of the limitation of the accelerometer force balance theory due to the unaccountability of model dynamics, the force recovery technique proposed herein is found simple to implement and can be opted as a tool for prediction of the aerodynamic coefficients and force time histories.

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Attarzadeh. „A Novel Algorithmic Cost Estimation Model Based on Soft Computing Technique“. Journal of Computer Science 6, Nr. 2 (01.02.2010): 117–25. http://dx.doi.org/10.3844/jcssp.2010.117.125.

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Devi R., Meena, und L. Premalatha. „Soft Computing Technique of Bridgeless SEPIC Converter for PMBLDC Motor Drive“. International Journal of Power Electronics and Drive Systems (IJPEDS) 9, Nr. 4 (01.12.2018): 1503. http://dx.doi.org/10.11591/ijpeds.v9.i4.pp1503-1509.

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A novel speed controller for the three-phase Brushless DC (BLDC) Motor Drive is proposed using a closed-loop AC-DC Bridgeless SEPIC Converter in continuous Conduction mode. This design proposes a single stage AC-DC converter with ON and OFF state equivalent circuits for 400W, 48V at 2450 rpm PMBLDC motor drive. The Fuzzy based voltage and current controlling method is proposed in this design. The voltage controlling method is used to control the speed for BLDC motor and the current controlling method is used to improve the power factor in AC supply. The speed of BLDC motor is observed with voltage disturbance and the constant motor speed is maintained. The proposed control method on SEPIC converter fed PMBLDC motor drive is modeled by Simulink/Matlab.

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Sakthi, S., J. Niresh, K. Vignesh und G. Anand Raj. „Development of Semi-Automatic Lathe by using Intelligent Soft Computing Technique“. IOP Conference Series: Materials Science and Engineering 324 (März 2018): 012053. http://dx.doi.org/10.1088/1757-899x/324/1/012053.

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Sokolov Mladenović, Svetlana, Igor Mladenović, Miloš Milovančević und Nebojša Denić. „Cross-cultural dimensions influence on business internationalization by soft computing technique“. Computers in Human Behavior 75 (Oktober 2017): 865–69. http://dx.doi.org/10.1016/j.chb.2017.06.035.

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Haoxiang, Dr Wang, und Dr Smys S. „QOS ENHANCED ROUTING PROTOCOLS FOR VEHICULAR NETWORK USING SOFT COMPUTING TECHNIQUE“. Journal of Soft Computing Paradigm 2019, Nr. 2 (19.12.2019): 91–102. http://dx.doi.org/10.36548/jscp.2019.2.004.

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The vehicular network that scopes to reach the internet access to make use of the available service on road along with the improvement in the safety and convenience while travelling has become a very popular area of research as they lay as the foundation for the intelligent transportation system. But due to the high mobility of the vehicles the network topology remains dynamic and the connectivity between the vehicles keep on changing reducing the life expectancy of the links and failures in the delivery of the services. So this paves way for the entailment of the more effective routing protocol that would heighten the vehicular network quality of service. The proposed method in the paper puts forth the modified Ant colony optimization that integrates the clustering technique to frame optimal shortest route for the internet access in the vehicular network to manage its mobility and the topology changes. The process of optimal route selection taking into consideration the mobility and the dynamic topology is evaluated using the network simulator tool to prove the capabilities of the proposed method on the grounds of bandwidth utilization throughput delay and link life expectancy.

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Topuz, Vedat, und A. Fevzi Baba. „Soft computing technique for power control of Triga Mark-II reactor“. Expert Systems with Applications 38, Nr. 9 (September 2011): 11201–8. http://dx.doi.org/10.1016/j.eswa.2011.02.167.

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Khatri, Jitesh Kumar, und Jyoti Kumar. „Soft Computing Technique for Product Design Suggestion in Smart Manufacturing Industry“. Asian Review of Mechanical Engineering 4, Nr. 1 (05.05.2015): 38–42. http://dx.doi.org/10.51983/arme-2015.4.1.2388.

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With the phenomenal growth in consumer durables industry, and more than ever before change and dynamism in the variety of design needs within the constraints of very limited time and investment, the urgent need of design automation is being recognized all over the world. To be able to capture the market through proactive launching of customer desirable products, the whole development cycle needs to be shrunk without any compromise of product features. This work suggests a fuzzy logic and ANN based noble design suggestion system which would assist the designers with a range of 3D design suggestions to choose from and take forward based on variable inputs from the market. This would facilitate faster designs through automation, reducing the overall design cycle time. The inputs for the design suggestions are based on anthropometry study and emotional behavior. Fuzzy logic is an efficient tool to choose the impact factor of a suggestion from a particular methodology based on the associated membership function andANN has good learning capability and combination of both forms an efficient system for design suggestion.

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Choudhury, Balamati, Sangeetha Manickam und R. M. Jha. „Soft Computing Techniques for Mutual Coupling Reduction in Metamaterial Antenna Array“. Journal of Optimization 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/252806.

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Application of soft computing techniques for various metamaterial designs and optimizations is an emerging field in the microwave regime. In this paper, a global optimization technique, namely, particle swarm optimization (PSO), is used for the design and optimization of a square split ring resonator (SSRR) having a resonant frequency of 2.4 GHz. The PSO optimizer yields the structural parameters, which is further simulated and validated with the optimized value. This optimized structure results in the mutual coupling reduction in a microstrip antenna array designed for wireless application.

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Neshe, Nishat Nabila Haque, und Manwinder Singh. „Hybrid Spectrum Sensing Techniques in 5G Cognitive Radio Networks in Soft Computing: A Review“. Computational Intelligence and Machine Learning 2, Nr. 1 (20.04.2021): 40–49. http://dx.doi.org/10.36647/ciml/02.01.a006.

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This paper describes an updated and efficient method for Hybrid spectrum sensing in cognitive radio (CR) system utilizing soft computing paradigms. The suggested soft computing approach utilizes an artificial neural network and for learning and decision making as a solution to the problems when a new product is subjected to the CR framework, developed the ability for unlicensed cognitive users to access radio frequencies through a spectrum hole and understand its implications through mechanisms spectrum sensing. The suggested soft computing approach could then be referred to as the Neuro technique. The need for higher bandwidth is important with the rise in the number of communication devices. Usage of cognitive radio for the fifth generation 5G communication network of the next generation Consider the fact that CR technology will efficiently optimize the use of much of the unused communication spectrum bands for the future 5G of wireless network and beyond. Keyword : Hybrid spectrum sensing, 5G Cognitive Radio (CR), ANN technique.

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