Approximation of walking robot stability model

Abstract eng:
The paper compares global and local approximation methods used for walking robot stability model. Global approximators are represented by feedforward multilayer neural network (FFNN) trained by gradient method; local approximators are represented by Locally Weighted Regression (LWR) and Receptive Field Weighted Regression (RFWR) methods. Global approximators try to learn global non-linear function which fits all the training data (minimizes training error), while local approximators use spatially limited data in query point neighborhood to generate appropriate response. Various aspects of used approximation methods are discussed (precision, robustness, computational and memory requirements).

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