𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Neural control experiments via dynamic neural algorithms

✍ Scribed by J. Fernández De Cañete; A. García-Cerezo; I. García-Moral

Publisher
John Wiley and Sons
Year
1999
Tongue
English
Weight
336 KB
Volume
13
Category
Article
ISSN
0890-6327

No coin nor oath required. For personal study only.

✦ Synopsis

Neural static and dynamic training algorithms have been extensively applied to the control and identi"cation of non-linear dynamic plants. In the present paper an extension of the static Marquardt learning algorithm, termed Dynamic Marquardt algorithm (DMA) is derived for the on-line training of neural networks with feedforward and feedback components. The performance of the method has been demonstrated by the neural control of a highly non-linear experimental #uid level. A stability analysis of the overall control scheme has been carried out using the conicity stability criterion. It has been found that the Dynamic Marquardt algorithm is much more e$cient than Dynamic backpropagation, when the relative size of the net is bounded.

📜 SIMILAR VOLUMES

Dynamical control by recurrent neural ne
Dynamical control by recurrent neural networks through genetic algorithms
✍ Toru Kumagai; Mitsuo Wada; Ryoichi Hashimoto; Akio Utsugi 📂 Article 📅 1999 🏛 John Wiley and Sons 🌐 English ⚖ 135 KB 👁 2 views

In this study we composed a recurrent neural network learning controller and applied it to the swinging up and stabilization problem of the inverted pendulum. A recurrent neural network was trained by a genetic algorithm which had an internal copy operator or inter-individual copy operator. An appro

Optimal control algorithm and neural net
Optimal control algorithm and neural network for dynamic groundwater management
✍ Hone-Jay Chu; Liang-Cheng Chang 📂 Article 📅 2009 🏛 John Wiley and Sons 🌐 English ⚖ 126 KB

## Abstract Researchers have found that obtaining optimal solutions for groundwater resource‐planning problems, while simultaneously considering time‐varying pumping rates, is a challenging task. This study integrates an artificial neural network (ANN) and constrained differential dynamic programmi

Control of Neural Information Transmissi
Control of Neural Information Transmission by Synaptic Dynamics
✍ BO CARTLING 📂 Article 📅 2002 🏛 Elsevier Science 🌐 English ⚖ 429 KB

The computational processing of a neural system is strongly in#uenced by the dynamical characteristics of the information transmission between neurons. In this work, the control of neural information transmission by synaptic dynamics is investigated by means of a masterequation-based stochastic mode