Sigma-Pi Neural Networks:Error Correction Methods
نویسندگان
چکیده
منابع مشابه
Lp approximation of Sigma-Pi neural networks
A feedforward Sigma-Pi neural network with a single hidden layer of m neurons is given by mSigma(j=1) cjg (nPi(k=1) xk-thetak(j)/lambdak(j)) where cj, thetak(j), lambdak are elements of R. In this paper, we investigate the approximation of arbitrary functions f: Rn-->R by a Sigma-Pi neural network in the Lp norm. An Lp locally integrable function g(t) can approximate any given function, if and ...
متن کاملEffects of Occam's Razor in Evolving Sigma-Pi Neural Nets
Several evolutionary algorithms make use of hierarchical representations of variable size rather than linear strings of xed length. Variable complexity of the structures provides an additional representa-tional power which may widen the application domain of evolutionary algorithms. The price for this is, however, that the search space is open-ended and solutions may grow to arbitrarily large s...
متن کاملUniform Approximation Capabilities of Sum-of-Product and Sigma-Pi-Sigma Neural Networks
Investigated in this paper are the uniform approximation capabilities of sum-of-product (SOPNN) and sigma-pi-sigma (SPSNN) neural networks. It is proved that the set of functions that are generated by an SOPNN with its activation function in C(R) is dense in C(K) for any compact K ∈ R , if and only if the activation function is not a polynomial. It is also shown that if the activation function ...
متن کاملA Modified Sigma-Pi-Sigma Neural Network with Adaptive Choice of Multinomials
Sigma-Pi-Sigma neural networks (SPSNNs) as a kind of high-order neural networks can provide more powerful mapping capability than the traditional feedforward neural networks (Sigma-Sigma neural networks). In the existing literature, in order to reduce the number of the Pi nodes in the Pi layer, a special multinomial Ps is used in SPSNNs. Each monomial in Ps is linear with respect to each partic...
متن کاملBlack-box Identity Testing for Low Degree Unmixed $\Sigma\Pi\Sigma\Pi(k)$ Circuits
A ΣΠΣΠ(k) circuit C = ∑k i=1 Fi = ∑k i=1 ∏di j=1 fij is unmixed if for each i ∈ [k], Fi = fi1(x1) · · · fin(xn), where each fij is a univariate polynomial given in the sparse representation. In this paper, we give a polynomial time black-box algorithm of identity testing for the low degree unmixed ΣΠΣΠ(k) circuits. In order to obtain the black-box algorithm, we first show that a special class o...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Procedia Computer Science
سال: 2018
ISSN: 1877-0509
DOI: 10.1016/j.procs.2018.11.077