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Linear Approximation in Frequency Domain

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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Linearization and Approximation

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A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...

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Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Published on: October 10, 2025

Radial basis function networks with linear interval regression weights for symbolic interval data.

Shun-Feng Su¹, Chen-Chia Chuang, C W Tao

¹Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|August 24, 2011

Summary

This summary is machine-generated.

This study presents a novel radial basis function network (RBFN) structure for modeling symbolic interval-valued data. The new RBFN effectively handles interval data using modified Gaussian functions and linear interval regression weights, demonstrating strong performance in experiments.

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Area of Science:

Artificial Intelligence
Machine Learning
Data Modeling

Background:

Symbolic interval-valued data presents unique challenges for traditional modeling techniques.
Existing methods may not adequately capture the uncertainty and range inherent in interval data.

Purpose of the Study:

To introduce a novel Radial Basis Function Network (RBFN) structure capable of modeling symbolic interval-valued data.
To enhance RBFNs by incorporating interval distance measures and linear interval regression weights.

Main Methods:

Modification of Gaussian functions within RBFNs to incorporate an interval distance measure.
Replacement of standard synaptic weights with linear interval regression weights that consider interval bounds, center, and range.
A two-stage learning mechanism involving interval competitive agglomeration clustering and gradient-descent optimization.

Main Results:

The proposed RBFN structure effectively models symbolic interval-valued data.
Experimental results, evaluated using root mean square error and correlation coefficient via Monte Carlo simulations, demonstrate the model's effectiveness.
The two-stage learning process successfully optimizes network parameters and regression coefficients.

Conclusions:

The novel RBFN structure offers a robust solution for modeling symbolic interval-valued data.
The integration of interval-specific distance measures and regression weights significantly improves modeling capabilities.
The proposed approach provides a computationally effective method for analyzing and predicting with interval-valued datasets.