Advanced Methods of Physiological System Modeling: Volume 3

Author:   V.Z. Marmarelis ,  V. Z. Marmarelis
Publisher:   Springer Science+Business Media
Edition:   1994 ed.
ISBN:  

9780306448195


Pages:   272
Publication Date:   31 October 1994
Format:   Hardback
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Our Price $567.60 Quantity:  
Add to Cart

Share |

Advanced Methods of Physiological System Modeling: Volume 3


Overview

Full Product Details

Author:   V.Z. Marmarelis ,  V. Z. Marmarelis
Publisher:   Springer Science+Business Media
Imprint:   Kluwer Academic/Plenum Publishers
Edition:   1994 ed.
Dimensions:   Width: 17.80cm , Height: 1.90cm , Length: 25.40cm
Weight:   1.290kg
ISBN:  

9780306448195


ISBN 10:   030644819
Pages:   272
Publication Date:   31 October 1994
Audience:   College/higher education ,  Professional and scholarly ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

Nonlinear Modeling of Physiological Systems Using Principal Dynamic Modes.- Experimental Basis for an Input/Output Model of the Hippocampal Formation.- Computational Methods of Neuronal Network Decomposition.- An Extension of the M-Sequence Technique for the Analysis of Multi-Input Nonlinear Systems.- Examples of the Investigation of Neural Information Processing by Point Process Analysis.- Testing a Nonlinear Model of Sensory Adaptation with a Range of Step Input Functions.- Identification of Nonlinear System with Feedback Structure.- Identification of Multiple-Input Nonlinear Systems Using Non-White Test Signals.- Nonlinear System Identification of Hippocampal Neurons.- Parametric and Nonparametric Nonlinear Modeling of Renal Autoregulation Dynamics.- Identification of Parametric (NARMAX) Models from Estimated Volterra Kernels.- Equivalence between Nonlinear Differential and Difference Equation Models Using Kernel Invariance Methods.- On Kernel Estimation Using Non-Gaussian and/or Non-White Input Data.- On the Relation between Volterra Models and Feedforward Artificial Neural Networks.- Three Conjectures on Neural Network Implementations of Volterra Models (Mappings).- Contributors.

Reviews

Author Information

Tab Content 6

Author Website:  

Countries Available

All regions
Latest Reading Guide

NOV RG 20252

 

Shopping Cart
Your cart is empty
Shopping cart
Mailing List