An Introduction to Neural Networks by Kroese B., van der Smagt P.

By Kroese B., van der Smagt P.

Show description

Read Online or Download An Introduction to Neural Networks PDF

Similar introduction books

Egyptian Myth: A Very Short Introduction (Very Short Introductions)

Egyptian myths articulated the center values of 1 of the most lasting civilizations in background, and myths of deities comparable to Isis and Osiris encouraged modern cultures and have become a part of the Western cultural history. Egyptian Mythology: a truly brief creation explains the cultural and historic history to the attention-grabbing and complicated global of Egyptian delusion, with every one bankruptcy facing a selected subject matter.

Latin for Local History: An Introduction 1st ed.

Latin for neighborhood historical past presents a self-teaching consultant for these historians who desire to take on the language within which nearly all of pre-eighteenth century old files were written. it really is specified in dealing basically with Latin present in old documents of the medieval interval. perform fabric and routines are supplied within the type of records most typically encountered via the historian of their learn - deeds, charters, courtroom rolls, bills, bishops' registers etc.

Introduction to computational neurobiology and clustering

This quantity presents scholars with the mandatory instruments to higher comprehend the fields of neurobiological modeling, cluster research of proteins and genes. the idea is defined ranging from the start and within the most simple phrases, there are lots of routines solved and never invaluable for the certainty of the speculation.

Additional resources for An Introduction to Neural Networks

Example text

I = gTi+1 gi+1 gTi gi gk = −∇f |pk with for all k ≥ 0. 33) Next, calculate pi+2 = pi+1 + λi+1 ui+1 where λi+1 is chosen so as to minimise f (p i+2 )3 . , see (Stoer & Bulirsch, 1980)). The process described above is known as the Fletcher-Reeves method, but there are many variants which work more or less the same (Hestenes & Stiefel, 1952; Polak, 1971; Powell, 1977). 6). Powell introduced some improvements to correct for behaviour in non-quadratic systems. The resulting cost is O(n) which is significantly better than the linear convergence 4 of steepest descent.

2). 4), the weight update must be changed to implement a shift towards the input: wk (t + 1) = wk (t) + γ(x (t) − wk (t)). 6) Again only the weights of the winner are updated. A point of attention in these recursive clustering techniques is the initialisation. Especially if the input vectors are drawn from a large or high-dimensional input space, it is not beyond imagination that a randomly initialised weight vector w o will never be chosen as the winner and will thus never be moved and never be used.

For a given transformation y = d(x ), we can divide the set of all possible input vectors into two classes: X + = { x | d(x ) = 1 } and X − = { x | d(x ) = −1 }. 19) Since there are N input units, the total number of possible input vectors x is 2 N . 7. CONCLUSIONS 31 is equal to 1 for x p = wh only. Similarly, the weights to the output neuron can be chosen such that the output is one as soon as one of the M predicate neurons is one: M yop = sgn h=1 yh + M − 1 2 . 21) This perceptron will give y o = 1 only if x ∈ X + : it performs the desired mapping.

Download PDF sample

Rated 4.86 of 5 – based on 8 votes