In this paper, the Airfoil polynomials for solving the second order integro-differential equation with a singular kernel is considered. The collocation method is developed to obtain an approximate solution of the equation. We present an error analysis and conclude by providing numerical tests to verify our results.
{"title":"Solving Fredholm Second Order Integro-Differential Equation with Logarithmic Kernel Using the Airfoil Collocation Method","authors":"N. E. Ramdani, A. Hadj","doi":"10.47836/mjms.16.1.07","DOIUrl":"https://doi.org/10.47836/mjms.16.1.07","url":null,"abstract":"In this paper, the Airfoil polynomials for solving the second order integro-differential equation with a singular kernel is considered. The collocation method is developed to obtain an approximate solution of the equation. We present an error analysis and conclude by providing numerical tests to verify our results.","PeriodicalId":43645,"journal":{"name":"Malaysian Journal of Mathematical Sciences","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41857583","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}
The main aim of this article is to establish some new refinements of Hermite Hadmard type inequalities via coordinate preinvex functions for fractional integrals. Here we give special cases to our results.
{"title":"Hermite-Hadamard Fejér Inequalities for Fractional Integrals for Functions Whose Second-Order Mixed Derivatives are Coordinated Preinvex","authors":"S. Mehmood, F. Zafar, H. Humza, A. Rasheed","doi":"10.47836/mjms.16.1.12","DOIUrl":"https://doi.org/10.47836/mjms.16.1.12","url":null,"abstract":"The main aim of this article is to establish some new refinements of Hermite Hadmard type inequalities via coordinate preinvex functions for fractional integrals. Here we give special cases to our results.","PeriodicalId":43645,"journal":{"name":"Malaysian Journal of Mathematical Sciences","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42167088","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}
Kho L. C., Kasihmuddin M. S. M., Mansor M. A., Sathasivam S.
Minimizing the cost function that corresponds to propositional logic is vital to ensure the learning phase of HNN can occur optimally. In that regard, optimal and non-biased algorithm is required to ensure HNN will always converge to global solution. Ant Colony Optimization (ACO) is a population-based and nature-inspired algorithm to solve various combinatorial optimization problems. ACO simulates the behaviour of the real ants that forage for food and communication of ants through pheromone density. In this work, ACO will be used to minimize the cost function that corresponds to the logical rule in Hopfield Neural Network. ACO will utilize pheromone density to find the optimal path that leads to zero cost function without consuming more learning iteration. Performance for all learning models will be evaluated based on various performance metrics. Results collected from computer simulation implies that ACO outperformed conventional learning model in minimizing the logical cost function.
{"title":"Propositional Satisfiability Logic via Ant Colony Optimization in Hopfield\u0000Neural Network","authors":"Kho L. C., Kasihmuddin M. S. M., Mansor M. A., Sathasivam S.","doi":"10.47836/mjms.16.1.04","DOIUrl":"https://doi.org/10.47836/mjms.16.1.04","url":null,"abstract":"Minimizing the cost function that corresponds to propositional logic is vital to ensure the learning phase of HNN can occur optimally. In that regard, optimal and non-biased algorithm is required to ensure HNN will always converge to global solution. Ant Colony Optimization (ACO) is a population-based and nature-inspired algorithm to solve various combinatorial optimization problems. ACO simulates the behaviour of the real ants that forage for food and communication of ants through pheromone density. In this work, ACO will be used to minimize the cost function that corresponds to the logical rule in Hopfield Neural Network. ACO will utilize pheromone density to find the optimal path that leads to zero cost function without consuming more learning iteration. Performance for all learning models will be evaluated based on various performance metrics. Results collected from computer simulation implies that ACO outperformed conventional learning model in minimizing the logical cost function.","PeriodicalId":43645,"journal":{"name":"Malaysian Journal of Mathematical Sciences","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46266594","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}