Pub Date : 2014-06-01DOI: 10.1504/IJAISC.2014.062814
Harish Sharma, Jagdish Chand Bansal, K. V. Arya
Artificial bee colony ABC optimisation algorithm is relatively a simple and recent population-based probabilistic approach for global optimisation. ABC has been outperformed over some nature inspired algorithms NIAs when tested over benchmark as well as real world optimisation problems. The solution search equation of ABC is significantly influenced by a random quantity which helps in exploration at the cost of exploitation of the search space. In the solution search equation of ABC, there is an enough chance to skip the true solution due to large step sizes. In order to balance the diversity and convergence capability of the ABC, in this paper, a power law-based local search strategy is proposed and integrated with ABC. The proposed strategy is named as power law-based local search in ABC PLABC. In the PLABC, new solutions are generated around the best solution and it helps to enhance the exploitation capability of ABC. Further, to improve the exploration capability, numbers of scout bees are increased. The experiments on 24 test problems of different complexities show that the proposed strategy outperforms the basic ABC and recent variants of ABC, namely, Gbest guided ABC GABC, best-so-far ABC BSFABC and modified ABC in most of the experiments.
{"title":"Power law-based local search in artificial bee colony","authors":"Harish Sharma, Jagdish Chand Bansal, K. V. Arya","doi":"10.1504/IJAISC.2014.062814","DOIUrl":"https://doi.org/10.1504/IJAISC.2014.062814","url":null,"abstract":"Artificial bee colony ABC optimisation algorithm is relatively a simple and recent population-based probabilistic approach for global optimisation. ABC has been outperformed over some nature inspired algorithms NIAs when tested over benchmark as well as real world optimisation problems. The solution search equation of ABC is significantly influenced by a random quantity which helps in exploration at the cost of exploitation of the search space. In the solution search equation of ABC, there is an enough chance to skip the true solution due to large step sizes. In order to balance the diversity and convergence capability of the ABC, in this paper, a power law-based local search strategy is proposed and integrated with ABC. The proposed strategy is named as power law-based local search in ABC PLABC. In the PLABC, new solutions are generated around the best solution and it helps to enhance the exploitation capability of ABC. Further, to improve the exploration capability, numbers of scout bees are increased. The experiments on 24 test problems of different complexities show that the proposed strategy outperforms the basic ABC and recent variants of ABC, namely, Gbest guided ABC GABC, best-so-far ABC BSFABC and modified ABC in most of the experiments.","PeriodicalId":364571,"journal":{"name":"Int. J. Artif. Intell. Soft Comput.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121086933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-06-01DOI: 10.1504/IJAISC.2014.062822
S. Anand, T. Raman, S. Subramanian
In chemical recovery boilers of paper mills, main steam outlet temperature control cannot be solved by straight forward automation control. As prior knowledge of the mechanism to maximise steam generation without affecting steam main temperature is unknown, a backpropogation supervisory neural network has been designed which exhibits a good degree of reinforcement learning. Various parameters considered encompassing concentration, composition and firing load of black liquor solids may not have ideal fixed values. Hence, a type 2 fuzzy logic model has been designed which in turn monitors the parameters and predicts the results. Errors are fed back iteratively through the backpropogation network, until the network learns the model. Fuzzy C-means clustering technique has been used to find coherent clusters. Then sensitivity analysis has been done to identify the parameters playing a significant role in obtaining the results. As it can be observed that the behaviour is stochastic, particle swarm optimisation has been implemented to optimise the combined effect of all parameters. Through this tool connecting steam attemperation control and smart soot blowing, clean heating surface is ensured resulting in enhanced green energy output and availability.
{"title":"Implementing a neuro fuzzy expert system for optimising the performance of chemical recovery boiler","authors":"S. Anand, T. Raman, S. Subramanian","doi":"10.1504/IJAISC.2014.062822","DOIUrl":"https://doi.org/10.1504/IJAISC.2014.062822","url":null,"abstract":"In chemical recovery boilers of paper mills, main steam outlet temperature control cannot be solved by straight forward automation control. As prior knowledge of the mechanism to maximise steam generation without affecting steam main temperature is unknown, a backpropogation supervisory neural network has been designed which exhibits a good degree of reinforcement learning. Various parameters considered encompassing concentration, composition and firing load of black liquor solids may not have ideal fixed values. Hence, a type 2 fuzzy logic model has been designed which in turn monitors the parameters and predicts the results. Errors are fed back iteratively through the backpropogation network, until the network learns the model. Fuzzy C-means clustering technique has been used to find coherent clusters. Then sensitivity analysis has been done to identify the parameters playing a significant role in obtaining the results. As it can be observed that the behaviour is stochastic, particle swarm optimisation has been implemented to optimise the combined effect of all parameters. Through this tool connecting steam attemperation control and smart soot blowing, clean heating surface is ensured resulting in enhanced green energy output and availability.","PeriodicalId":364571,"journal":{"name":"Int. J. Artif. Intell. Soft Comput.","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123343221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-06-01DOI: 10.1504/IJAISC.2014.062816
Madhuri Arya, Kusum Deep, Jagdish Chand Bansal
The selection of an appropriate strategy for adjusting inertia weight w is one of the most effective ways of enhancing the performance of particle swarm optimisation PSO. Recently, a new idea, inspired from social behaviour of humans, for adaptation of inertia weight in PSO, has been proposed, according to which w adapts itself as the improvement in best fitness at each iteration. The same idea has been implemented in two different ways giving rise to two inertia weight variants of PSO namely globally adaptive inertia weight GAIW PSO, and locally adaptive inertia weight LAIW PSO. In this paper, the performance of these two variants has been compared with three other inertia weight variants of PSO employing an extensive test suite of 15 benchmark global optimisation problems. The experimental results establish the supremacy of the proposed variants over the existing ones in terms of convergence speed, and computational effort. Also, LAIW PSO comes out to be the best performer out of all the algorithms considered in this study.
{"title":"A nature inspired adaptive inertia weight in particle swarm optimisation","authors":"Madhuri Arya, Kusum Deep, Jagdish Chand Bansal","doi":"10.1504/IJAISC.2014.062816","DOIUrl":"https://doi.org/10.1504/IJAISC.2014.062816","url":null,"abstract":"The selection of an appropriate strategy for adjusting inertia weight w is one of the most effective ways of enhancing the performance of particle swarm optimisation PSO. Recently, a new idea, inspired from social behaviour of humans, for adaptation of inertia weight in PSO, has been proposed, according to which w adapts itself as the improvement in best fitness at each iteration. The same idea has been implemented in two different ways giving rise to two inertia weight variants of PSO namely globally adaptive inertia weight GAIW PSO, and locally adaptive inertia weight LAIW PSO. In this paper, the performance of these two variants has been compared with three other inertia weight variants of PSO employing an extensive test suite of 15 benchmark global optimisation problems. The experimental results establish the supremacy of the proposed variants over the existing ones in terms of convergence speed, and computational effort. Also, LAIW PSO comes out to be the best performer out of all the algorithms considered in this study.","PeriodicalId":364571,"journal":{"name":"Int. J. Artif. Intell. Soft Comput.","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125274991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-06-01DOI: 10.1504/IJAISC.2014.062827
Narendran Rajagopalan, C. Mala
The usage of mobile communications systems has grown exponentially. But, the bandwidth available for mobile communications is finite. Hence, there is a desperate attempt to optimise the channel assignment schemes. In this work, some of the quality of service parameters such as residual bandwidth, number of users, duration of calls, frequency of calls, priority, time of calls and mean opinion score are considered. Genetic algorithm and artificial neural networks is used to determine the optimal channel assignment considering the quality of service parameters. The simulation results show that genetic algorithm performs better than frequency assignment at random, a heuristic method. But application of artificial neural networks outperforms genetic algorithm and frequency assignment at random method by a considerable margin. Channel allocation can be optimised using these soft computing techniques resulting in better throughput.
{"title":"Channel allocation scheme for cellular networks using evolutionary computing","authors":"Narendran Rajagopalan, C. Mala","doi":"10.1504/IJAISC.2014.062827","DOIUrl":"https://doi.org/10.1504/IJAISC.2014.062827","url":null,"abstract":"The usage of mobile communications systems has grown exponentially. But, the bandwidth available for mobile communications is finite. Hence, there is a desperate attempt to optimise the channel assignment schemes. In this work, some of the quality of service parameters such as residual bandwidth, number of users, duration of calls, frequency of calls, priority, time of calls and mean opinion score are considered. Genetic algorithm and artificial neural networks is used to determine the optimal channel assignment considering the quality of service parameters. The simulation results show that genetic algorithm performs better than frequency assignment at random, a heuristic method. But application of artificial neural networks outperforms genetic algorithm and frequency assignment at random method by a considerable margin. Channel allocation can be optimised using these soft computing techniques resulting in better throughput.","PeriodicalId":364571,"journal":{"name":"Int. J. Artif. Intell. Soft Comput.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133411885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-06-01DOI: 10.1504/IJAISC.2014.062828
N. Birla, A. Swarup
This paper proposes the design of fixed structure preview controller with multiple objectives in constrained environment using hybrid technique based on co-evolutionary particle swarm optimisation and marriage in honey bees optimisation algorithm. The paper, also, presents a comparative evaluation of the commonly used constraint - handling approaches in evolutionary algorithms with the proposed hybrid multi-objective constrained co-evolutionary particle swarm optimisation MOCC-PSO procedure. The available procedures and the proposed algorithm are evaluated and verified using MATLAB platform for engineering design problems, namely autonomous control of under-water vehicle and 2-DOF helicopter. The results validate the ability of the algorithm in terms of the quality of solution obtained in the constrained environment and the ease to implement the non-classical objectives and constraints.
{"title":"Fixed structure constrained preview control design using enhanced PSO approach","authors":"N. Birla, A. Swarup","doi":"10.1504/IJAISC.2014.062828","DOIUrl":"https://doi.org/10.1504/IJAISC.2014.062828","url":null,"abstract":"This paper proposes the design of fixed structure preview controller with multiple objectives in constrained environment using hybrid technique based on co-evolutionary particle swarm optimisation and marriage in honey bees optimisation algorithm. The paper, also, presents a comparative evaluation of the commonly used constraint - handling approaches in evolutionary algorithms with the proposed hybrid multi-objective constrained co-evolutionary particle swarm optimisation MOCC-PSO procedure. The available procedures and the proposed algorithm are evaluated and verified using MATLAB platform for engineering design problems, namely autonomous control of under-water vehicle and 2-DOF helicopter. The results validate the ability of the algorithm in terms of the quality of solution obtained in the constrained environment and the ease to implement the non-classical objectives and constraints.","PeriodicalId":364571,"journal":{"name":"Int. J. Artif. Intell. Soft Comput.","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126088498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-06-01DOI: 10.1504/IJAISC.2014.062824
R. Jayashree, K. S. Murthy, B. Anami
Stop words are high frequency words in a document, which add unrealistic requirement on the classifier, both in terms of time and space complexity. There has been considerable amount of work done in information retrieval in English, but information retrieval in the Kannada language is a new concept. The identification and removal of stop words in the Kannada language could be an important piece of work, as elimination of stop words would definitely reduce the feature space, which in turn would help in reducing time and space complexity. It is to be noted that there is no standard stop word list in the Kannada language. This warrants us to take up this task of developing an algorithm for removing structurally similar stop words. The stop word removal though reduces feature space, may not contribute to the improvement in the performance of the classifiers as is evident from our results.
{"title":"Effect of stop word removal on the performance of naïve Bayesian methods for text classification in the Kannada language","authors":"R. Jayashree, K. S. Murthy, B. Anami","doi":"10.1504/IJAISC.2014.062824","DOIUrl":"https://doi.org/10.1504/IJAISC.2014.062824","url":null,"abstract":"Stop words are high frequency words in a document, which add unrealistic requirement on the classifier, both in terms of time and space complexity. There has been considerable amount of work done in information retrieval in English, but information retrieval in the Kannada language is a new concept. The identification and removal of stop words in the Kannada language could be an important piece of work, as elimination of stop words would definitely reduce the feature space, which in turn would help in reducing time and space complexity. It is to be noted that there is no standard stop word list in the Kannada language. This warrants us to take up this task of developing an algorithm for removing structurally similar stop words. The stop word removal though reduces feature space, may not contribute to the improvement in the performance of the classifiers as is evident from our results.","PeriodicalId":364571,"journal":{"name":"Int. J. Artif. Intell. Soft Comput.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126191531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-06-01DOI: 10.1504/IJAISC.2014.062826
S. Chandrakanth, T. Chelliah, S. P. Srivastava
The exact knowledge of some of the induction motor parameters is very important to implement efficient control schemes and to determine the efficiency. These parameters can be obtained by no-load test that is not easily possible for the motors working in process industries where continuous operation is required. Here, particle swarm optimisation is used for in situ efficiency determination of induction motor 5 hp without performing no-load test. The part load efficiency and power factor can be improved through loss model controller where the motor excitation is adjusted in accordance with load and speed. Induction motor parameters vary with temperature. So, parameters obtained by conducting no load and blocked rotor test may vary with loading of induction motor. LMC is sensitive to parameter variation and its performance is affected when parameters change. An attempt is made to gain a deeper physical insight into the induction motor operation through sensitivity analysis of its equivalent circuit parameters. This study reveals the effect each of the circuit parameters namely R
{"title":"Efficiency determination of induction motor and its sensitivity analysis towards parameter variation","authors":"S. Chandrakanth, T. Chelliah, S. P. Srivastava","doi":"10.1504/IJAISC.2014.062826","DOIUrl":"https://doi.org/10.1504/IJAISC.2014.062826","url":null,"abstract":"The exact knowledge of some of the induction motor parameters is very important to implement efficient control schemes and to determine the efficiency. These parameters can be obtained by no-load test that is not easily possible for the motors working in process industries where continuous operation is required. Here, particle swarm optimisation is used for in situ efficiency determination of induction motor 5 hp without performing no-load test. The part load efficiency and power factor can be improved through loss model controller where the motor excitation is adjusted in accordance with load and speed. Induction motor parameters vary with temperature. So, parameters obtained by conducting no load and blocked rotor test may vary with loading of induction motor. LMC is sensitive to parameter variation and its performance is affected when parameters change. An attempt is made to gain a deeper physical insight into the induction motor operation through sensitivity analysis of its equivalent circuit parameters. This study reveals the effect each of the circuit parameters namely R","PeriodicalId":364571,"journal":{"name":"Int. J. Artif. Intell. Soft Comput.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125241944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-02-01DOI: 10.1504/IJAISC.2014.059282
Madhuri Arya, Kusum Deep
In this paper, particle swarm optimisation PSO is applied for optimising the yields of three useful bioactive compounds in the extract of the fruits of Gardenia, a Chinese herb. Two of these compounds are used as natural colouring agents in food and medicine whereas the third, having high anti-oxidant capacity, is used in drugs for the cure of many diseases. The yields of these compounds in Gardenia extract are dependent on three process variables, namely, extraction temperature, extraction time and ethanol concentration, defining the extraction conditions. In this study, PSO is used to determine the optimum extraction conditions, i.e., the values of the three process variables that will produce optimum yields of the three bioactive compounds. Most of the work in this direction has used response surface methodology. But the results of our simulations show that PSO is better suited for the problem at hand.
{"title":"Extraction process optimisation using particle swarm algorithm","authors":"Madhuri Arya, Kusum Deep","doi":"10.1504/IJAISC.2014.059282","DOIUrl":"https://doi.org/10.1504/IJAISC.2014.059282","url":null,"abstract":"In this paper, particle swarm optimisation PSO is applied for optimising the yields of three useful bioactive compounds in the extract of the fruits of Gardenia, a Chinese herb. Two of these compounds are used as natural colouring agents in food and medicine whereas the third, having high anti-oxidant capacity, is used in drugs for the cure of many diseases. The yields of these compounds in Gardenia extract are dependent on three process variables, namely, extraction temperature, extraction time and ethanol concentration, defining the extraction conditions. In this study, PSO is used to determine the optimum extraction conditions, i.e., the values of the three process variables that will produce optimum yields of the three bioactive compounds. Most of the work in this direction has used response surface methodology. But the results of our simulations show that PSO is better suited for the problem at hand.","PeriodicalId":364571,"journal":{"name":"Int. J. Artif. Intell. Soft Comput.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116947829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-02-01DOI: 10.1504/IJAISC.2014.059280
K. Deb, Debayan Deb
Mutation is an important operator in genetic algorithms GAs, as it ensures maintenance of diversity in evolving populations of GAs. Real-parameter GAs RGAs handle real-valued variables directly without going to a binary string representation of variables. Although RGAs were first suggested in early '90s, the mutation operator is still implemented variable-wise - in a manner that is independent to each variable. In this paper, we investigate the effect of five different mutation schemes for RGAs using two different mutation operators - polynomial and Gaussian mutation operators. Based on extensive simulation studies, it is observed that a mutation clock implementation is computationally quick and also efficient in finding a solution close to the optimum on four different problems used in this study for both mutation operators. Moreover, parametric studies with their associated parameters reveal suitable working ranges of the parameters. Interestingly, both mutation operators with their respective optimal parameter settings are found to possess a similar inherent probability of offspring creation, a matter that is believed to be the reason for their superior working. This study signifies that the long suggested mutation clock operator should be considered as a valuable mutation operator for RGAs.
{"title":"Analysing mutation schemes for real-parameter genetic algorithms","authors":"K. Deb, Debayan Deb","doi":"10.1504/IJAISC.2014.059280","DOIUrl":"https://doi.org/10.1504/IJAISC.2014.059280","url":null,"abstract":"Mutation is an important operator in genetic algorithms GAs, as it ensures maintenance of diversity in evolving populations of GAs. Real-parameter GAs RGAs handle real-valued variables directly without going to a binary string representation of variables. Although RGAs were first suggested in early '90s, the mutation operator is still implemented variable-wise - in a manner that is independent to each variable. In this paper, we investigate the effect of five different mutation schemes for RGAs using two different mutation operators - polynomial and Gaussian mutation operators. Based on extensive simulation studies, it is observed that a mutation clock implementation is computationally quick and also efficient in finding a solution close to the optimum on four different problems used in this study for both mutation operators. Moreover, parametric studies with their associated parameters reveal suitable working ranges of the parameters. Interestingly, both mutation operators with their respective optimal parameter settings are found to possess a similar inherent probability of offspring creation, a matter that is believed to be the reason for their superior working. This study signifies that the long suggested mutation clock operator should be considered as a valuable mutation operator for RGAs.","PeriodicalId":364571,"journal":{"name":"Int. J. Artif. Intell. Soft Comput.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125534283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-02-01DOI: 10.1504/IJAISC.2014.059288
S. Tapaswini, S. Chakraverty
This paper targets to investigate the numerical solution of linear, non-linear and system of ordinary differential equations with fuzzy initial condition. Here, two Euler type methods have been proposed in order to obtain numerical solution of the fuzzy differential equations. Along with this, an exact solution methodology is also discussed. Obtained results are depicted in term of plots to show the efficiency of the proposed methods. The solutions are compared with the known results and are found to be in good agreement.
{"title":"Euler-based new solution method for fuzzy initial value problems","authors":"S. Tapaswini, S. Chakraverty","doi":"10.1504/IJAISC.2014.059288","DOIUrl":"https://doi.org/10.1504/IJAISC.2014.059288","url":null,"abstract":"This paper targets to investigate the numerical solution of linear, non-linear and system of ordinary differential equations with fuzzy initial condition. Here, two Euler type methods have been proposed in order to obtain numerical solution of the fuzzy differential equations. Along with this, an exact solution methodology is also discussed. Obtained results are depicted in term of plots to show the efficiency of the proposed methods. The solutions are compared with the known results and are found to be in good agreement.","PeriodicalId":364571,"journal":{"name":"Int. J. Artif. Intell. Soft Comput.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130340576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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