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Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

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Planning with a functional neural network architecture.

D A Panagiotopoulos¹, R W Newcomb, S K Singh

¹Automation Department, Technological and Educational Institute of Thessaloniki, Thessaloniki, Greece.

IEEE Transactions on Neural Networks

|February 7, 2008

Summary

This summary is machine-generated.

This study introduces a planning system for interactive environments where a responder system generates behavior based on a challenger system. The planner uses a functional artificial neural network (FANN) to ensure smooth system responses.

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Area of Science:

Control Systems Engineering
Artificial Intelligence
Nonlinear System Dynamics

Background:

Interactive systems involve a challenger and a responder, where the responder's behavior is governed by a response function.
Nonlinear systems, often modeled by Volterra series, present challenges in predicting and controlling their input-output (I/O) relationships.

Purpose of the Study:

To develop a planning framework for controlling a responder system in an interactive environment.
To utilize a functional artificial neural network (FANN) for modeling the responder's I/O relation.
To generate smooth and predictable behavior in the responder system through advanced planning.

Main Methods:

A planner was designed to generate future input sequences for the responder.
The planner utilizes an estimated challenger output sequence, the response function, and a FANN model of the responder's I/O map.
The responder integrates planner inputs with potential feedback to produce its output.

Main Results:

The planning approach enables the control of the responder system's behavior.
The FANN architecture effectively models the nonlinear I/O relation of the responder.
The planner's effectiveness in generating smooth behavior was demonstrated through a practical example.

Conclusions:

Planning is crucial for achieving smooth behavior in interactive systems.
Functional artificial neural networks provide an effective method for implementing the planner.
The proposed planning strategy offers a viable solution for controlling complex nonlinear systems.