Using AdaBoost-based Multiple Functional Neural Fuzzy Classifiers Fusion for Classification Applications

© The Authors, published by EDP Sciences, 2018. In this study, two intelligent classifiers, the AdaBoost-based incremental functional neural fuzzy classifier (AIFNFC) and the AdaBoost-based fixed functional neural fuzzy classifier (AFFNFC), are proposed for solving the classification problems. The AIFNFC approach will increase the amount of functional neural fuzzy classifiers based on the corresponding error during the training phase; while the AFNFC approach is equipped with a fixed amount of functional neural fuzzy classifiers. Then, the weights of AdaBoost procedure are assigned for classifiers. The proposed methods are applied to different classification benchmarks. Results of this study demonstrate the effectiveness of the proposed AIFNFC and AFFNFC methods

Penerapan Kontroler Neural Fuzzy Untuk Pengendalian Kecepatan Motor Induksi 3 Fasa Pada Mesin Sentrifugal

Motor induksi 3 fasa adalah alat penggerak yang paling banyak digunakan dalam dunia industri. Salah satu aplikasi motor induksi pada industri gula adalah pada mesin sentrifugal yang digunakan pada proses sentrifugasi. Mesin sentrifugal menggunakan motor induksi sebagai penggerak untuk memutar chamber mesin sentrifugal yang berisi massacuite yang akan diolah. Tujuan penelitian ini adalah merancang kontroler neural fuzzy yang digunakan untuk mengendalikan kecepatan motor induksi sehingga dapat mempertahankan kecepatan sesuai setpoint walaupun terjadi perubahan beban. Motor induksi dimodelkan dengan menggunakan transformasi dq dan algoritma kontroler disimulasikan dengan MATLAB. Kontroler neural fuzzy menggunakan 2 layer dengan jumlah neuron yang diubah yaitu 20 dan 50 neuron. Dari hasil simulasi didapatkan, kontroler neural fuzzy 50 neuron memiliki settling time lebih cepat tetapi error steady state lebih besar dan tidak menghasilkan overshoot pada respon kecepatan. Sedangkan pada kondisi terbebani, kontroler dapat mempertahankan kecepatan sesuai dengan setpoint. Waktu pemulihan kontroler 50 neuron lebih cepat dibandingkan kontroler neural fuzzy dengan 20 neuron

Route selection for vehicle navigation and control

This paper presents an application of neural-fuzzy methodology for the problem of route selection in a typical vehicle navigation and control system. The idea of the primary attributes of a route is discussed, and a neural-fuzzy system is developed to help a user to select a route out of the many possible routes from an origin to the destination. The user may not adopt the recommendation provided by the system and choose an alternate route. One novel feature of the system is that the neural-fuzzy system can adapt itself by changing the weights of the defined fuzzy rules through a training procedure. Two examples are given in this paper to illustrate how the route selection/ranking system can be made adaptive to the past choice or preference of the user. ©2007 IEEE.published_or_final_versio

Neural fuzzy repair : integrating fuzzy matches into neural machine translation

We present a simple yet powerful data augmentation method for boosting Neural Machine Translation (NMT) performance by leveraging information retrieved from a Translation Memory (TM). We propose and test two methods for augmenting NMT training data with fuzzy TM matches. Tests on the DGT-TM data set for two language pairs show consistent and substantial improvements over a range of baseline systems. The results suggest that this method is promising for any translation environment in which a sizeable TM is available and a certain amount of repetition across translations is to be expected, especially considering its ease of implementation

Development of a Neural-Fuzzy Model for Machinability Data Selection in Turning Process

A neural-fuzzy model has been developed to represent machinability data selection in turning process. Turning process is a branch of machining process, which is used to produce cylindrical parts. Considerable efforts have been done to automate such machining process in order to increase the efficiency and precision of manufacturing. One of the issues is machinability data selection, which is always referred as the proper selection of cutting tools and machining parameters. This task is a complex process; and usually depends on the experience and skill of a machinist. Although sources like machining data handbooks and tool catalogues are available for reference, the process is still very much depending on a skilled machinist. Previously, mathematical and empirical approaches have been attempted to reduce the dependency. However, the complexity of machining makes it difficult to formulate a proper model. Applications of fuzzy logic and neural network have been considered too to solve the machining problem; and have shown good potential. But, some issues remain unaddressed. In fuzzy logic, among the issues are tedious process of rules identification and inability to self-adapt to changing machining conditions. On the other hand, neural network has the issues of black box problem and difficulty in optimal topology determination. In order to overcome these difficulties, a neural-fuzzy model is proposed to model machinist in selecting machinability data for turning process. The neural-fuzzy model combines the self-adapting and learning abilities of neural network with the human-like knowledge representation and explanation abilities of fuzzy logic into one integrated system. The characteristics of fuzzy logic would solve the shortcomings in neural network; and vice versa. Generally, the developed neural-fuzzy model is designed to have five layers; input and output layers, and three hidden layers. Each of the layers has different classes of nodes; in which are input nodes, input term nodes, rule nodes, output term nodes and output nodes. The model is developed using Microsoft Visual C++ .NET (MSVC++ .NET). Object oriented approach is applied as the development process to enhance reusability. The results from the model have been validated and compared against machining data of Machining Data Handbook from Metcut Research Associate. Good correlations have been shown, indicating the feasibility of representing machining data selection with neural-fuzzy model. The mean absolute percentage error for four different types of tools is below 3%, and averaging at 2.4%. Apart from that, the extracted fuzzy rules are compared with the general rules of thumbs in turning process as well as rules from other paradigm; and found to be consistent. This would simplify the task of obtaining fuzzy rules from machining data. Beside that, the model is compared with other artificial intelligence approaches, such as fuzzy logic, neural network and genetic algorithm. The neural-fuzzy model has shown good result among them. In addition, the characteristics of the model are studied and analyzed as well; in which include membership functions, shouldered membership functions and randomness. This research has shown promising results in employing neural-fuzzy model to solve problems; in this case, machinability data selection in turning process. The developed neural-fuzzy model should be further considered in a wider range of real-world machining processes for learning and prescribing knowledge