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Journal ArticleDOI

Intelligent Techniques for Simulation and Modelling

TL;DR: This paper presents a meta-modelling system that automates the very labor-intensive and therefore time-heavy and expensive and therefore expensive and expensive process of computer programming called “solution-side programming” (SLP).
Abstract: 1 School of Engineering and Technology, Central Queensland University, North Rockhampton, QLD 4702, Australia 2 Center for Biometrics Research, Southern Polytechnic State University, Atlanta, GA 30060-2896, USA 3 College of Computer Science and Information Technology, Northeast Normal University, Changchun, Jilin 130017, China 4 School of Science, Information Technology and Engineering, Federation University, Ballarat, VIC 3353, Australia

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Journal ArticleDOI
31 May 2019
TL;DR: Considering the emerging technology in the agriculture field, the future trends of crop simulation models are discussed and the different crop Simulation models in details are discussed.
Abstract: Variation in the climatic conditions is the major hurdle in the Agriculture sector to attain high crop yield. The Crop simulation models portray the stage-wise growth of crop with the respective environment condition. The crop simulation models help the farmer to make better decisions for improving the crop yield. Artificial Intelligence, Data mining and Computational Intelligent are becoming more prominent in the agriculture field for decision making because of emerging technology such as GIS, Satellite data and remote sensing data in agriculture. This paper reviews information on crop simulation models using computational intelligence and their application. It also reviews the different types of crop simulation models and their limitation in Agriculture. It also discusses the different crop simulation models in details. Considering the emerging technology in the agriculture field we discussed the future trends of crop simulation models.

2 citations


Cites background from "Intelligent Techniques for Simulati..."

  • ...The growth of computational intelligence in recent years has increased the use of these techniques in different domain of engineering disciplines [2]....

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References
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Book
01 Jan 1996
TL;DR: This review discusses mathematics, linear programming, and set--Constrained and Unconstrained Optimization, as well as methods of Proof and Some Notation, and problems with Equality Constraints.
Abstract: Preface. MATHEMATICAL REVIEW. Methods of Proof and Some Notation. Vector Spaces and Matrices. Transformations. Concepts from Geometry. Elements of Calculus. UNCONSTRAINED OPTIMIZATION. Basics of Set--Constrained and Unconstrained Optimization. One--Dimensional Search Methods. Gradient Methods. Newton's Method. Conjugate Direction Methods. Quasi--Newton Methods. Solving Ax = b. Unconstrained Optimization and Neural Networks. Genetic Algorithms. LINEAR PROGRAMMING. Introduction to Linear Programming. Simplex Method. Duality. Non--Simplex Methods. NONLINEAR CONSTRAINED OPTIMIZATION. Problems with Equality Constraints. Problems with Inequality Constraints. Convex Optimization Problems. Algorithms for Constrained Optimization. References. Index.

3,283 citations

Journal ArticleDOI
TL;DR: Experimental results demonstrate that the proposed clustering algorithm will generate better clustering results than those of FCM and FWCM algorithms, in particularly for hyperspectral images.
Abstract: Fuzzy clustering model is an essential tool to find the proper cluster structure of given data sets in pattern and image classification. In this paper, a new weighted fuzzy C-Means (NW-FCM) algorithm is proposed to improve the performance of both FCM and FWCM models for high-dimensional multiclass pattern recognition problems. The methodology used in NW-FCM is the concept of weighted mean from the nonparametric weighted feature extraction (NWFE) and cluster mean from discriminant analysis feature extraction (DAFE). These two concepts are combined in NW-FCM for unsupervised clustering. The main features of NW-FCM, when compared to FCM, are the inclusion of the weighted mean to increase the accuracy, and, when compared to FWCM, the centroid of each cluster is included to increase the stability. The motivation of this work is to meliorate the well-known fuzzy C-Means algorithm (FCM) and a recently proposed fuzzy weighted C-Means algorithm (FWCM). Our finding is that the proposed algorithm gives greater classification accuracy and stability than that of FCM and FWCM. Experimental results on both synthetic and real data demonstrate that the proposed clustering algorithm will generate better clustering results than those of FCM and FWCM algorithms, in particularly for hyperspectral images.

108 citations

Journal ArticleDOI
TL;DR: A new hybrid algorithm, cooperative genetic ant system (CGAS) is proposed to deal with the travelling salesman problem and shows that CGAS has superior performance over other GA and ACO algorithms for solving TSPs in terms of capability and consistency of achieving the global optimal solution, and quality of average optimal solutions, particularly for small T SPs.
Abstract: The travelling salesman problem (TSP) is a classic problem of combinatorial optimization and has applications in planning, scheduling, and searching in many scientific and engineering fields. Ant colony optimization (ACO) has been successfully used to solve TSPs and many associated applications in the last two decades. However, ACO has problem in regularly reaching the global optimal solutions for TSPs due to enormity of the search space and numerous local optima within the space. In this paper, we propose a new hybrid algorithm, cooperative genetic ant system (CGAS) to deal with this problem. Unlike other previous studies that regarded GA as a sequential part of the whole searching process and only used the result from GA as the input to subsequent ACO iterations, this new approach combines both GA and ACO together in a cooperative manner to improve the performance of ACO for solving TSPs. The mutual information exchange between ACO and GA in the end of the current iteration ensures the selection of the best solutions for next iteration. This cooperative approach creates a better chance in reaching the global optimal solution because independent running of GA maintains a high level of diversity in next generation of solutions. Compared with results from other GA/ACO algorithms, our simulation shows that CGAS has superior performance over other GA and ACO algorithms for solving TSPs in terms of capability and consistency of achieving the global optimal solution, and quality of average optimal solutions, particularly for small TSPs.

97 citations

Journal ArticleDOI
TL;DR: An expectation-maximization (EM) algorithm called emBayesB which allows only a proportion of SNP to be in LD with QTL and incorporates prior information about the distribution of SNP effects and its accuracy is similar to Bayesian methods but it takes only a fraction of the time.
Abstract: Background: The information provided by dense genome-wide markers using high throughput technology is of considerable potential in human disease studies and livestock breeding programs. Genome-wide association studies relate individual single nucleotide polymorphisms (SNP) from dense SNP panels to individual measurements of complex traits, with the underlying assumption being that any association is caused by linkage disequilibrium (LD) between SNP and quantitative trait loci (QTL) affecting the trait. Often SNP are in genomic regions of no trait variation. Whole genome Bayesian models are an effective way of incorporating this and other important prior information into modelling. However a full Bayesian analysis is often not feasible due to the large computational time involved. Results: This article proposes an expectation-maximization (EM) algorithm called emBayesB which allows only a proportion of SNP to be in LD with QTL and incorporates prior information about the distribution of SNP effects. The posterior probability of being in LD with at least one QTL is calculated for each SNP along with estimates of the hyperparameters for the mixture prior. A simulated example of genomic selection from an international workshop is used to demonstrate the features of the EM algorithm. The accuracy of prediction is comparable to a full Bayesian analysis but the EM algorithm is considerably faster. The EM algorithm was accurate in locating QTL which explained more than 1% of the total genetic variation. A computational algorithm for very large SNP panels is described. Conclusions: emBayesB is a fast and accurate EM algorithm for implementing genomic selection and predicting complex traits by mapping QTL in genome-wide dense SNP marker data. Its accuracy is similar to Bayesian methods but it takes only a fraction of the time. Background Genome-wide association (GWA) studies are being used more often for risk prediction in humans and trait prediction in livestock. Such studies associate individual single nucleotide polymorphisms (SNP) from a dense genome-wide panel with between-individual variation in traits. The GWA provides measures of strength of association and estimates of the size of the effect of each SNP even though SNP identified as being of predictive value are unlikely to be causative. These GWA studies have had limited success as the individual effects of loci are often small and relatively few loci pass the very stringent statistical testing criteria imposed. The detected variants can be used to construct genetic profiles [1,2] but jointly the loci identified often explain less than 10% of the phenotypic variance [2-4]. This small fraction of variance explained is due in part to the stringent statistical thresholds required for identification in GWA studies [5]. Nevertheless the scope of the genomic information provided by high throughput technology using dense SNP panels remains of considerable potential. Researchers in other fields, in particular animal and plant breeding, have developed methods of prediction of genetic value that use all available marker information simultaneously and do not apply such stringent tests of statistical significance [6,7]. Thus, instead of testing hundreds of thousands of separate hypotheses of 'is this single SNP associated with the trait' as in GWA, the problem is

44 citations

BookDOI
01 Apr 2003
TL;DR: In this paper, a review of Calculus and Ordinary Differential Equations Series Solutions and Special Functions Complex Variables Vector and Tensor Analysis Partial Differentials Equations I Partial Differentially Equations II Numerical Methods Numerically Solution of Partial Differential Elements Calculus of Variations Special Topics
Abstract: Review of Calculus and Ordinary Differential Equations Series Solutions and Special Functions Complex Variables Vector and Tensor Analysis Partial Differential Equations I Partial Differential Equations II Numerical Methods Numerical Solution of Partial Differential Equations Calculus of Variations Special Topics.

38 citations


"Intelligent Techniques for Simulati..." refers background or methods in this paper

  • ...Modelling and simulation are two different but closely related approaches in many disciplines, which are illustrated in Figure 1 [1]....

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  • ...Figure 1: The procedure of modelling and simulation (modified from [1])....

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