Neural Systems for Control


Book Description

Control problems offer an industrially important application and a guide to understanding control systems for those working in Neural Networks. Neural Systems for Control represents the most up-to-date developments in the rapidly growing aplication area of neural networks and focuses on research in natural and artifical neural systems directly applicable to control or making use of modern control theory. The book covers such important new developments in control systems such as intelligent sensors in semiconductor wafer manufacturing; the relation between muscles and cerebral neurons in speech recognition; online compensation of reconfigurable control for spacecraft aircraft and other systems; applications to rolling mills, robotics and process control; the usage of past output data to identify nonlinear systems by neural networks; neural approximate optimal control; model-free nonlinear control; and neural control based on a regulation of physiological investigation/blood pressure control. All researchers and students dealing with control systems will find the fascinating Neural Systems for Control of immense interest and assistance. - Focuses on research in natural and artifical neural systems directly applicable to contol or making use of modern control theory - Represents the most up-to-date developments in this rapidly growing application area of neural networks - Takes a new and novel approach to system identification and synthesis







Application Of Neural Networks And Other Learning Technologies In Process Engineering


Book Description

This book is a follow-up to the IChemE symposium on “Neural Networks and Other Learning Technologies”, held at Imperial College, UK, in May 1999. The interest shown by the participants, especially those from the industry, has been instrumental in producing the book. The papers have been written by contributors of the symposium and experts in this field from around the world. They present all the important aspects of neural network utilisation as well as show the versatility of neural networks in various aspects of process engineering problems — modelling, estimation, control, optimisation and industrial applications.




Scientific and Technical Aerospace Reports


Book Description

Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.










Application of Machine Learning in Process Control in Optical Fiber Manufacturing


Book Description

The current era of big data and IoT has propelled the manufacturing industry to the era of "Industry 4.0". This thesis presents an approach to manufacturing process control through the use of Machine Learning models in the optical fiber manufacturing industry. Utilizing measured production data from the fiber drawing tower, a long short-term memory (LSTM) neural network structure is used to find the correlation between the inputs and outputs of the process. Different experiments were conducted on the physical draw tower and the simulation to gauge the accuracy of the model and how well it mimics the plant's performance. This thesis, then, presents an in-depth investigation to the deployment of the digital twin model on an industrial PLC in order to control the diameter of the produced optic fiber at a given setpoint. The model would be able to predict and anticipate changes in the diameter and adjust the gains on the PLC to keep the process under control. This could potentially replace the iterative and laborious process of controller tuning and serve as a tool to be widely utilized in manufacturing settings.




Advanced Applications for Artificial Neural Networks


Book Description

In this book, highly qualified multidisciplinary scientists grasp their recent researches motivated by the importance of artificial neural networks. It addresses advanced applications and innovative case studies for the next-generation optical networks based on modulation recognition using artificial neural networks, hardware ANN for gait generation of multi-legged robots, production of high-resolution soil property ANN maps, ANN and dynamic factor models to combine forecasts, ANN parameter recognition of engineering constants in Civil Engineering, ANN electricity consumption and generation forecasting, ANN for advanced process control, ANN breast cancer detection, ANN applications in biofuels, ANN modeling for manufacturing process optimization, spectral interference correction using a large-size spectrometer and ANN-based deep learning, solar radiation ANN prediction using NARX model, and ANN data assimilation for an atmospheric general circulation model.




Advances in Process Control III


Book Description