A bstract We quantify the role of scrambling in quantum machine learning. characterize a neural network’s (QNNs) error terms properties via out-of-time-ordered correlator (OTOC). network can be trained by minimizing loss function. show that function bounded OTOC. prove gradient This demonstrates OTOC landscape regulates trainability QNN. numerically this is flat for maximally QNNs, which pose c...

In this paper, adaptive neural network sliding-mode controller design approach with decoupled method is proposed. The decoupled method provides a simple way to achieve asymptotic stability for a class of fourth-order nonlinear system. The adaptive neural sliding mode control system is comprised of neural network (NN) and a compensation controller. The NN is the main tracking controller, which i...

This paper deals with the designing of neural PID speed controller of Permanent Magnet synchronous Motor (PMSM). The conventional Proportional-Integral-Derivative (PID) controller is largely used in industry because of the robustness this regulator procures. ANN is developed controller in this work, offer inherent advantages over conventional PID controller for PMSM, Reduction of the effects of...

In this paper, a neural network based predictive controller is designed to govern the dynamics of a heat exchanger pilot plant. Heat exchanger is a highly nonlinear process; therefore, a nonlinear prediction method can be a better match in a predictive control strategy. Advantages of neural networks for the process modeling are studied and a neural network based predictor is designed, trained a...

Software Defined Network (SDN) is a new architecture for network management and its main concept is centralizing network management in the network control level that has an overview of the network and determines the forwarding rules for switches and routers (the data level). Although this centralized control is the main advantage of SDN, it is also a single point of failure. If this main contro...

This paper presents a novel approach to control the speed of BLDC motor by using PID, Fuzzy, Neural Network and Anti-windup Controllers. The PID controllers is set to optimize the motor parameters such as rise time, peak time, Maximum peak overshoot and Settling time. The fuzzy controller adopts fuzzy logic to retune the PID parameters. Based on the mathematical model of BLDC Motor, novel adapt...

In this paper, a fuzzy adaptive neural-network model-following speed controller for permanent-magnet synchronous motor (PMSM) drives is proposed. The fuzzy neuralnetwork model-following controller (FNNMFC) consist of a proportional plus integral (PI) like-fuzzy controller in addition to an on-line trained neural-network model-following controller (NNMFC). This controller, FNNMFC, combines the m...