An adaptive neuro-fuzzy sliding mode controller for MIMO systems with disturbance

This paper introduces the mathematical model of ammonia and urea reactors and suggested three methods for designing a special purpose controller. The first proposed method is Adaptive model predictive controller, the second is Adaptive Neural Network Model Predictive Control, and the third is Adaptive neuro-fuzzy sliding mode controller. These methods are applied to a multivariable nonlinear system as an ammonia–urea reactor system. The main target of these controllers is to achieve stabilization of the outlet concentration of ammonia and urea, a stable reaction rate, an increase in the conversion of carbon monoxide(CO) into carbon dioxide(CO_2) to reduce the pollution effect, and an increase in the ammonia and urea productions, keeping the NH_3/CO_2 ratio equal to 3 to reduce the unreacted CO_2 and NH_3, and the two reactors' temperature in the suitable operating ranges due to the change in reactor parameters or external disturbance. Simulation results of the three controllers are compared. Comparative analysis proves the effectiveness of the suggested Adaptive neurofuzzy sliding mode controller than the two other controllers according to external disturbance and the change of parameters. Moreover, the suggested methods when compared with other controllers in the literature show great success in overcoming the external disturbance and the change of parameters.

Improving Date Fruit Classification Using Cycle GAN-Generated Dataset

Dates are an important part of human nutrition.Dates are high in essential nutrients and provide a number of health benefits.Date fruits are also known to protect against a number of diseases,including cancer and heart disease.Date fruits have several sizes,colors,tastes,and values.There are a lot of challenges facing the date producers.One of the most significant challenges is the classification and sorting of dates.But there is no public dataset for date fruits,which is a major limitation in order to improve the performance of convolutional neural networks(CNN)models and avoid the overfitting problem.In this paper,an augmented date fruits dataset was developed using Deep Convolutional Generative Adversarial Networks(DCGAN)and CycleGAN approach to augment our collected date fruit datasets.This augmentation is required to address the issue of a restricted number of images in our datasets,as well as to establish a balanced dataset.There are three types of dates in our proposed dataset:Sukkari,Ajwa,and Suggai.After dataset augmentation,we train our created dataset using ResNet152V2 and CNN models to assess the classification process for our three categories in the dataset.To train these two models,we start with the original dataset.Thereafter,the models were trained using the DCGAN-generated dataset,followed by the CycleGAN-generated dataset.The resulting results demonstrated that when using the ResNet152V2model,the CycleGAN-generated dataset had the highest classification performance with 96.8%accuracy,followed by the CNN model with 94.3%accuracy.