The light intensities of 25, 10, and 5 lux were used in the first, second, and third weeks of life, respectively Cobb, Water and feed were available ad libitum. The climate-controlled wind tunnels and cages were cleaned daily to avoid the accumulation of gases and making the environment compatible with the physiological needs of the animals Sousa et al.
The equipment was positioned above the cages externally to each tunnel. A plastic film was placed over each tunnel near the cage to allow image acquisition. The thermographic images were analyzed using Fluke Smart View software, and the average surface temperature t s was obtained on the surface of the head, neck, back, and wings of three birds, one in each division of the cage.
After data collection, fuzzy modeling was started by establishing the membership functions for data input t db and DTC and output t s. The fuzzy logic toolbox of MATLAB Ra has two types of controllers, Mamdani and Sugeno, and each controller allows performing defuzzification and membership functions for both input and output data.
Five defuzzification methods were used in Mamdani inference: center of gravity of the area centroid , the bisector of the area bisector , largest of maxima lom , middle of maxima mom , and smallest of maxima som. In contrast, the Sugeno controller has only two types of defuzzification: weighted average wtaver and weighted sum wtsum.
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Triangular and Gaussian membership functions were used for the input variables in both inference methods Figure 1. For the output variable, triangular and Gaussian membership functions were used for the Mamdani controller, and singletons functions were used for the Sugeno controller because these functions were representative of the input variables Figure 2.
The rule system was developed on the basis of the combinations of inputs total of 20 , which were assigned a weight of 1 to establish relevant equality for each rule and reach associations able to optimized the parameters Sargolzaei et al. The experimental data obtained using infrared thermography were used to test the fuzzy models.
In the first three weeks of life, the maintenance of thermal comfort conditions is essential in broiler chickens, and t s varies as a function of t db Abreu et al. The results of this study corroborate those of the above study, in which t s was increased or decreased when birds were subjected to high or low t db values, respectively, and this variation was dependent on the age of the animals. The statistical indices used to compare the systems and their respective configurations are shown in Table 1.
Defuzzification methods: right of gravity of the area centroid , the bisector of the area bisector , largest of maximum lom , middle of maximum mom , smallest of maximum som , weighted average wtaver , and weighted sum wtsum. Through the Mamdani inference system, the indicators of Gaussian functions were pointed out as better compared to those of triangular functions, regardless of the defuzzification method applied.
The best results were obtained using Sugeno inference, which provided numerically equal results for both triangular and Gaussian functions, except in cases in which wtsum was applied with Gaussian functions. Kisi found that Sugeno inference produced better estimates than Mamdani inference, and the analysis of reference evapotranspiration indicated that MAE, RMSE, and R 2 values were 0. In addition, the accuracy of Sugeno inference was higher than that of artificial neural networks and empirical models.
The studies on animal husbandry using broiler chickens and fuzzy composition and performance are shown in Table 2. The analysis of different fuzzy system configurations allowed testing the combinations of pertinence functions, inference and defuzzification methods, and determine the accuracy of these methods Table 1. The type of function should be selected according to data characteristics to achieve high representativeness.
Some data from triangular functions can be replaced with Gaussian functions. In addition, MATLAB provides a Gaussian membership function that can assume trapezoidal format, allowing the substitution without loss and errors of representativeness. The correlation graphs of t s values obtained experimentally and predicted by fuzzy systems and respective R 2 values are shown in Figures 3 and 4.
The distributions of absolute errors indicate that the t s values simulated by fuzzy systems were accurate because the errors were below 0. Ponciano et al. The response surfaces of three fuzzy systems with the smallest simulation errors Figure 7 indicate the characteristics of t s as a function of t db and DTC. The fuzzy systems triangular functions with wtaver and wtsum defuzzification and Gaussian functions with wtaver defuzzification represented by the response surfaces Figures 7a , b , and c yielded the same statistical indices when compared with the experimental data Table 1. In each case, values of the input numerical values from indicators in Table 1 and output classification final numerical evaluation of the project given by a set of experts through Delphi method are known.
The input and output data are normalized to values between 0 and 1. In the first set of experiments the genfis2 functionality is used to generate the initial rule base by grouping the input-output data. Table 2 shows the parameters used in each experiment with the genfis2 function. It has an initial base of 27 rules defined by expert judgment.
All the experiments were run in a terminal with a Core Duo 1. Forty iterations are applied with zero error tolerance and hybrid optimization method. To validate the training the option of testing with a different set of input-output pairs is used, applying a total of instances for training and 82 for validating. In considering the scheme of training error curves and validation error, the parameters of fuzzy sets are determined just before the iteration where the FIS over-training occurs. Through the training and validation of FIS, the parameters of the fuzzy sets of the rules are adjusted.
Once the experiments are applied, it is necessary to compare the results. In Table 3 training and validation errors are shown, which allows us to compare the performance of the 10 experiments performed to optimize the FIS project evaluator. Experiment 6 obtained the best results with an average error equal to 2. The average validation error 3.
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The predictive accuracy metric percentage of correctly classified projects is applied to measure the quality of classification in project evaluation given by each experiment, yielding the results shown in Figure 2. According to the data partitions 82 instances to validate experiments 2, 3, 6, and 7 get the best predictive accuracy with The FIS obtained from Experiment 6 is selected as a classification model of new cases for evaluating projects since it attains the lowest training error, lower validation error, and higher predictive accuracy.
As result, an adaptive-network-based fuzzy inference system provided by experiment 6 that optimizes project evaluation is obtained. To check the quality of the classification, a comparative analysis of results obtained by this NFS and the output given by the FIS of Lugo et al. From this analysis it is observed an improvement in the quality of the classification for project evaluation given by the NFS The implementation of ANFIS in Matlab has the following limitations: It only supports FIS of Sugeno Zero or One Grade type; the membership functions of the antecedents require to be derivable; the membership functions of the consequents claim to be of the same type linear or constant ; the consequents are different for each rule; the FIS has only one exit; the rules have unitary weight; and the initial base of rules must be known.
However, the main advantage of using the ANFIS of Matlab is that an adjusted FIS can be obtained from a set of training data pairs of input-output without requiring its generation by a human expert; besides, it also allows validation learning.
Fuzzy Inference System - Theory and Applications
NFS integration with the project management tool is achieved through the export files option of Mat-lab. The file. These data rules are stored in a table in the database and used every time the evaluation of new projects is done by the adjusted FIS. Once the FIS is trained, it is possible to introduce new projects and evaluate them properly. The proposed mechanism is valid to make the integration of ANFIS with any type of software project management tool.
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Project evaluation is a complex task that involves vagueness in concepts and uncertainty in information, a situation where the use of soft computing techniques yields good results. The developed adaptive-network-based fuzzy inference system allows the efficient adjustment of the existing rule base, increasing the quality of project evaluation. In addition, it makes it possible to preserve the knowledge of experts in organizations and to perform an effective control of project execution.
The application of ANFIS integrated with software project management tools as Xedro-GESPRO is a novel contribution, allowing raising the quality and competitiveness of the developed products, providing them with a high added value. The achieved result provides a contribution towards improving existing decision-making support tools into organizations to project-oriented production.
Furthermore, the use of machine learning methods for the evaluation of projects increases the adaptability of organizations faced with the changing management styles caused by the maturity reached during their continuous improvement. As future research we intend to work on different aspects such as the computational efficiency of the implementation of the ANFIS technique; the improvement of the optimization algorithm for calculating the fuzzy sets parameters; the incorporation of non-differentiable membership functions; and consideration of structural changes in the data.
Also, the implementation of new machine learning libraries for the evaluation and control of projects with open source soft-ware tools and programing languages such as PL-R, opens a field of research related to the improvement of the integration with decision-making tools in project management. Pennsylvania, Stanford: Gartner, Bello and J. Kelemen, Y Liang, and S. Franklin, "A comparative study of different machine learning approaches for decision making", Recent Advances in Simulation, Computational Methods and Soft Computing, pp.
Sugeno, Fuzzy measures and fuzzy integrals: A survey, fuzzy automata and decision processes. New York: Elsevier, Massachusetts: The Mathworks, [online]. Accessed on: March 7, Dweiri and M.
Adaptive Intuitionistic Fuzzy Inference Systems of Takagi-Sugeno Type for Regression Problems
Kablan, "Using fuzzy decision making for the evaluation of the project management internal efficiency", Decision Support Systems, vol. Certa, M.
Enea, and A. Giallanza, "A synthetic measure for the assessment of the project performance", in Business performance measurement and management. Berlin: Springer-Verlag, , pp. Salamanca: Universidad de Salamanca, Mewada, A.rekefolme.ml
Fuzzy Inference System: Theory and Applications - Google книги
Sinhal, and B. Technical Report. Lugo et al. Bermudez et al. Takagi and M. Services on Demand Article. This book is an attempt to accumulate the researches on diverse inter disciplinary field of engineering and management using Fuzzy Inference System FIS. The book is organized in seven sections with twenty two chapters, covering a wide range of applications. Section I, caters theoretical aspects of FIS in chapter one. Section II, dealing with FIS applications to management related problems and consisting three chapters.
Section III, accumulates six chapters to commemorate FIS application to mechanical and industrial engineering problems. Section IV, elaborates FIS application to image processing and cognition problems encompassing four chapters. Section V, describes FIS application to various power system engineering problem in three chapters. Section VI highlights the FIS application to system modeling and control problems and constitutes three chapters.
By Isabel L. By Sina Khanmohammadi and Javad Jassbi. Shahizan Othman and Yeap Chun Nyen. Zaki, O. Mahgoub, A.