Neuro-Fuzzy and. Soft Computing. A Computational Approach to Learning and Machine Intelligence. Jyh-Shing Roger Jang [email protected] Computer. Neuro-Fuzzy and Soft Computing provides the first comprehensive treatment of the constituent methodologies underlying neuro-fuzzy and soft computing. Introduction to Neuro, Fuzzy and Soft Computing, Fuzzy Sets: Basic Definition and Terminology,. Set-theoretic Operations, Member Function Formulation and.
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Soft computing is a branch of computer science that deals with a family of methods that imitate human intelligence. This is done with the goal of creating tools. Similar Free eBooks. Filter by page count, Jyh-Shing, Jang R., Neuro- Fuzzy and Soft Computing A Computational Approach to Learning and. Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications. Neuro-Fuzzy and Soft Computing (Jang Sun Mizutani) - Free ebook download as PDF File .pdf) or read book online for free. Redes Neuronales.
Like the other methods, FL has some problems while its application. One of the most important issues of FL is making decision s on its appropriate parameters. Therefore, all of these problems and lack of the knowledge, lead us to combine ANN with FL to minimize the error and make a better decision on FL's parameters.
The CANFIS model is the result of the combination of adaptable fuzzy inputs with a neural network in order to have a rapid and more accurate predictor. Actually, by this combination, it is possible to use both advantages of fuzzy inference systems with the explanatory nature of rules membership functions and ANN as a dynamic estimator.
Also, another reason for applying this technique for grade estimation is several problems which have been mentioned in geostatistical methods and also making some uncertainties in grade estimation by ANN.
Therefore, to skip this step and select the right parameters to reach the best performance, we applied the GA which shows a good potential in ANN optimization Gupta and Sexton, ; McInerney and Dhawan, ; Ishigami, ; Sexton et al. Therefore, hereafter the methodology will be demonstrated. Basically, FL and ANN are the model-free and nonlinear estimators that their aim is mostly achieving a stable and reliable model which can justify the noise and uncertainties in the complex data Yager and Zadeh, According to earlier discussions, it is obvious that some problems such as determining the shape and the location of membership functions MFs for each fuzzy variable are involved with FL.
The FL efficiency basically depends on the estimation of premise and the consequent parts. Besides, the problems like number of hidden layers, number of neurons in each hidden layer, learning rate and momentum coefficient are also involved with ANN modeling.
However, one of the most important capabilities of FL is to model the qualitative aspects of human by using the simple rules. In contrast, the ANN also have some advantages such as its capability of learning and high computational power.
However, Asadi and Tahmasebi presented a comprehensive study in which a global methodology for ANN is demonstrated. Furthermore, a sensitivity analysis on different ANN parameters can be found in their study. The purpose of this paper is to present a neuro-fuzzy approach for classifying students into different groups.
The neuro-fuzzy classifier used previous exam results and other related factors as input variables and labeled students based on their expected academic performance. The results showed that the proposed approach achieved a high accuracy.
The results were also compared with those obtained from other well-known classification approaches, including support vector machine, Naive Bayes, neural network, and decision tree approaches.
The comparative analysis indicated that the neuro-fuzzy approach performed better than the others. It is expected that this work may be used to support student admission procedures and to strengthen the services of educational institutions. Introduction Accurately predicting student performance is useful in many different contexts in educational environments.
When admission officers review applications, accurate predictions help them to distinguish between suitable and unsuitable candidates for an academic program. The failure to perform an accurate admission decision may result in an unsuitable candidate being admitted to the university. Since the quality of an educational institution is mainly reflected in its research and training, the quality of admitted candidates affects the quality level of an institution.
Accurate prediction enables educational managers to improve student academic performance by offering students additional support such as customized assistance and tutoring resources. The results of prediction can also be used by lecturers to specify the most suitable teaching actions for each group of students and provide them with further assistance tailored to their needs.
Thus, accurate prediction of student achievement is one way to enhance quality and provide better educational services. As a result, the ability to predict students' academic performance is important for educational institutions.
A very promising tool to achieve this objective is the use of data mining. Data mining processes large amounts of data to discover hidden patterns and relationships that support decision-making. Data mining in higher education is forming a new research field called educational data mining [ 1 , 2 ].
The application of data mining to education allows educators to discover new and useful knowledge about students [ 3 ]. Educational data mining develops techniques for exploring the types of data that come from educational institutions. The book is well suited for use as a text for courses on computational intelligence and as a single reference source for this emerging field. To help readers understand the material the presentation includes more than 50 examples, more than exercises, over illustrations, and more than Matlab scripts.
In addition, Matlab is utilized to visualize the processes of fuzzy reasoning, neural-network learning, neuro-fuzzy integration and training, and gradient-free optimization such as genetic algorithms, simulated annealing, random search, and downhill Simplex method. All Matlab scripts used in the book are available on the free companion software disk that may be ordered by using the enclosed reply card. The book also contains an "Internet Resource Page" to point the reader to on-line neuro-fuzzy and soft computing home pages, publications, public-domain software, research institutes, news groups, etc.
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