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The outlined procedure of approximating data using orthogonal basis functions (here polynomials) and clustering estimated coefficients appears quite naturally in the context of functional data analysis [ 9- 14] 14].
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In fact, the Picard iterative method is a well-known procedure for approximating the solution of stochastic differential equations (SDEs).
In 1953, Mann [1] introduced an iteration procedure for approximating fixed points of a nonexpansive mapping T in a Hilbert space.
This clinical report describes a modified sectional impression procedure and a method of approximating the sectional trays for a completely edentulous female patient with limited oral opening due to scleroderma.
Also, studies either on approximate fixed point or on qualitative aspects of numerical procedures for approximating fixed points are available in the literature; see [4, 20, 21].
Also, there have been developed studies on approximate fixed point or on qualitative aspects of numerical procedures for approximating fixed points see, for example [19, 20].
One of the most popular iteration procedures for approximating a fixed point of is Picard's iteration defined by.
This paper presents a kinematic procedure to synthesize planar mechanisms capable of approximating a shape change defined by a general set of curves.
The convergence condition of sampling procedure for approximate inference was investigated.
A simple procedure for approximate correction of concomitant gradient artifacts is described.
This procedure gradually approximates a given signal upon selection of an increasing number of elements from a predefined set of elementary signals.
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