Abstract—This paper presents modeling and control of a highly nonlinear system including, non-interacting two spherical tanks using iterative learning control (ILC). Consequently, the objective of the paper is to control the liquid levels in the nonlinear tanks. First, a proportional-integral-derivative (PID) controller is applied to the plant model as a suitable benchmark for comparison. Then,...

This paper presents our attempt to automatically define feedforward neural networks using genetic programming. Neural networks have been recognized as powerful approximation and classification tools. On the other hand, the genetic programming has been used effectively for the production of intelligent systems, such as the neural networks. In order to reduce the search space and guide the search...

conclusions as we can see the ann outputs values are very close to actual cu concentration, so indicating that predicted values are accurate and the network design is proper and the input variables well suitable for the prediction of cu concentration. background access to safe drinking water is one of the basic human rights and essential for healthy life. concerns about the effects of copper on...

This paper presents a storage-efficient learning model titled Recursive Binary Neural Networks for embedded and mobile devices having a limited amount of on-chip data storage such as hundreds of kilo-Bytes. The main idea of the proposed model is to recursively recycle data storage of weights (parameters) during training. This enables a device with a given storage constraint to train and instant...

Abstract We study the storage capacity of quantum neural networks (QNNs), described by completely positive trace preserving (CPTP) maps acting on an N -dimensional Hilbert space. demonstrate that attractor QNNs can store in a non-trivial manner up to linearly independent pure states. For n qubits, reach exponential capacity, <mml:math xmlns:mml="http://www.w3.or...

in recent years, researches on reinforcement learning (rl) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. neural network reinforcement learning (nnrl) is among the most popular algorithms in the rl framework. the advantage of using neural networks enables the rl to search for optimal policies more efficiently in several real-life applicat...

The Department of Energy’s River Protection Project (RPP) is tasked with retrieving highly radioactive waste from Hanford double-shell and single-shell tanks to provide feed for vitrification for long-term storage. Approximately 330,000 metric tons of sodium-rich radioactive waste originating from separation of plutonium from irradiated uranium fuel is stored in 177 underground tanks at Hanford...