Genetic Adaptive Neuro-fuzzy Inference System Research Articles

Efficient classification and grading of MANGOES with GANFIS for improved performance

The application of image processing has been extended to agricultural industries in recent times. As the concept of image processing has vast application in agricultural industries, fruit grading has been focused in this research. There are number of approaches available for the classification and grading of mangoes, they suffer to achieve higher performance in classification. The most approaches uses color features and shape features only. To improve the performance of classification and fruit grading, an efficient GANFIS (Genetic Adaptive Neuro Fuzzy Inference System) is presented in this paper. The GANFIS approach reads the 2D mango images and extract various features like color, shape and texture features from the input image. Over the features extracted, the genetic algorithm has been applied to perform feature selection. With the extracted features, the method applies adaptive neuro fuzzy inference technique to perform classification and grading. The classification algorithm estimates multi feature class similarity MFCS measure towards each class of mangoes to perform classification where the grading is performed based on the same being estimated within the class. The proposed GANFIS approach has achieved sensitivity (98.05%), specificity (97.39%) and Accuracy (99.18%).

Enhanced Neuro-Fuzzy System Based on Genetic Algorithm for Medical Diagnosis

Objective: The aim of this study was to optimize the performance of an Adaptive Neuro-Fuzzy Inference System (ANFIS) in terms of its connection weights which is usually computed based on trial and error when used to diagnose Typhoid fever patients. Methods: This research proposed the use of Genetic Algorithm (GA) technique to automatically evolve optimum connection weights needed to efficiently train a built ANFIS model used for Typhoid fever diagnosis. The GA module computes the best set of connection weights, stores them, and later supplies them to the corresponding hidden layer nodes for training the ANFIS. The medical record of 104 Typhoid fever patients aged 15 to 75 were used to evaluate the performance of the multi-technique decision support system. 70% of the dataset was used training data, 15% was used for validation while the remaining 15% was used to observe the performance of the proposed system. Results: From the evaluation results, the proposed Genetic Adaptive Neuro Fuzzy Inference System (GANFIS) achieved an average diagnosis accuracy of 92.7% compared to 85.4% recorded by the ANFIS method. It was equally observed that the diagnosis time was much lower for the proposed method when compared to that of ANFIS. Conclusion: Therefore, the proposed system (GANFIS) has the capability to attenuate the key problems associated with Neuro-Fuzzy Based diagnostic methods if fully embraced and as well it could be adopted to solve challenging problems in several other domains.

Analysis and synthesis of laser forming process using neural networks and neuro-fuzzy inference system

To apply laser forming process in reality, it is required to know the relationships between the deformed shape and scanning paths along with heating conditions. The deformation due to laser scanning depends on various factors, namely laser power, scan speed, spot diameter, scan position, number of scans, and many others. This article presents soft computing-based methods to predict deformations for a set of heating conditions, and also to determine the heating lines and heat conditions, in order to get a desired shape (i.e., inverse analysis). A novel attempt has been made in this paper to carry out analysis and synthesis (inverse analysis) of laser forming process using both genetic-neural network (GA-NN) and genetic adaptive neuro-fuzzy inference system (GA-ANFIS). During the analysis, laser power, scan speed, spot diameter, scan position and number of scans are taken as inputs and bending angle is considered as the output. A batch mode of training has been used for both the approaches with the help of some experimental data. The performances of the developed approaches have been tested on some real experimental data. Both the approaches are found to be effective to predict the bending angles and carry out the process synthesis successfully. GA-NN approach is found to perform better than the GA-ANFIS approach in predicting the bending angles, and both the approaches are able to provide comparable predictions in inverse analysis.

Modeling and control of V/f controlled induction motor using genetic-ANFIS algorithm

This paper deals with modeling and performance analysis of the voltage/frequency (V/f) control of induction motor drives. The V/f control, which realizes a low cost and simple design, is advantageous in the middle to high-speed range. Its torque response depends on the electrical time constant of the motor and adjustments of the control parameters are not need. Therefore, V/f control of induction motor is carried out. Space vector pulse width modulation is used for controlling the motor because of including minimum harmonics according to the other PWM techniques. Proportional Integral (PI) controller is used to control speed of induction motor. In this work, optimization of PI coefficients is carried out by Ziegler-Nichols model and Genetic-Adaptive Neuro-Fuzzy Inference System (ANFIS) model. These controllers are applied to drive system with 0.55 kW induction motor. A digital signal processor controller (dsPIC30F6010) is used to carry out control applications. The proposed method is compared Ziegler-Nichols model. Experimental results show the effectiveness of the proposed control method.