The performance of the nonlinear control system that is subjected to uncertainty, can be enhanced by implementing an adaptive approach by using the robust output-feedback control and the artificial intelligence neural network. This paper seeks to utilize output feedback control for nonlinear system using artificial intelligence employing neural network. The Two Wheel Mobile Robot (TWMR) is treated as a multi-body dynamic system. The nonlinear swing-up problem is handled by designing an adaptive neural network, which is trained using a modified conventional controller called Linear Quadratic Optimal State Estimator with Integral Control (LQOSEIC). In this paper, the nonlinear system TWMR is stabilized utilizing a robust output feedback control called LQOSEIC. This controller allows a linearized model to emulate a model reference for the original nonlinear system. However, it works for a limited range of operations and will fail if the plant characteristics are unknown or uncertain. An adaptive neural network is used to overcome this problem. The adaptive neural controller is trained offline using LQOSEIC to obtain the initial weights of neurons for the network's hidden layers. After finishing the training, the LQOSEIC will be replaced by the adaptive neural controller. The main advantage of a neuro-controller is its ability to update the weights online depending on the error signal. If there are any disturbances or uncertainties that arises within the concerned nonlinear system, the neuro-controller will be able to handle it because of online learning that compensates for the effect of unpredictable conditions. The proposed adaptive neural network improves control performance and ensures the robust stability of the closed-loop control system. Finally, numerical simulations are used to demonstrate the efficacy of the proposed controllers.

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The advancement of technology has made it possible for modern cars to utilize an increasing number of processing systems. Many methods have been developed recently to detect traffic signs using image processing algorithms. This study deals with an experiment to build a CNN model which can classify traffic signs in real-time effectively using OpenCV. The experimentation method involves building a CNN model based on a modified LeNet architecture with four convolutional layers, two max-pooling layers and two dense layers. The model is trained and tested with the German Traffic Sign Recognition Benchmark (GTSRB) dataset. Parameter tuning with different combinations of learning rate and epochs is done to improve the model’s performance. Later, this model is used to classify the images introduced to the camera in real- time. The graphs depicting the accuracy and loss of the model before and after parameter tuning are presented. Also, an experiment was performed to classify the traffic sign image introduced to the camera by using the CNN model. A high probability score is achieved during the process.

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This paper approaches the use of a Virtual Assistant using neural networks for recognition of commonly used words. The main purpose is to facilitate the users’ daily lives by sensing the voice and interpreting it into action. Alice, which is the name of the assistant, is implemented based on four main techniques: Hot word detection, Voice to Text conversion, Intent recognition, and Text to Voice conversion. Linux is the operating system of choice, for developing and running the assistant because it is in the public domain, also, Linux has been implemented on most Single-board computers. Python is chosen as a development language due to its capabilities and compatibility with various APIs and libraries, which are deemed necessary for the project. The virtual assistant will be required to communicate with IoT devices. In addition, a speech recognition system is created in order to recognize the significant technical words. An artificial neural network (ANN) with different structure networks and training algorithms is utilized in conjunction with the Mel Frequency Cepstral Coefficient (MFCC) feature extraction technique to increase the identification rate effectively and find the optimal performance. For training purposes, the Levenberg-Marquardt (LM) and BGFS Quasi-Newton Resilient Backpropagation are compared using 10 MFCC, utilizing from 10 to 50 neurons increasing in increments of 10 similarly for 13MFCC the training is done utilizing from between 10 to 50 neurons.