By Hugh M. Cartwright B.Sc, PhD (auth.), David J. Livingstone (eds.)
As an extension of man-made intelligence learn, synthetic neural networks (ANN) objective to simulate clever habit by way of mimicking the way in which that organic neural networks functionality. In Artificial Neural Networks, a global panel of specialists document the heritage of the appliance of ANN to chemical and organic difficulties, offer a advisor to community architectures, education and the extraction of principles from knowledgeable networks, and canopy many state-of-the-art examples of the applying of ANN to chemistry and biology. within the culture of the hugely profitable Methods in Molecular Biology™ sequence, this quantity shows transparent, easy-to-use info with many step by step laboratory protocols.
Comprehensive and cutting-edge, Artificial Neural Networks is a wonderful advisor to this accelerating technological box of study.
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Additional info for Artificial Neural Networks: Methods and Applications
The input vectors are relayed to the Kohonen network. This means that they are compared with the neurons organized in a special way in the single layer Kohonen network. To enable the comparison between the input vectors and neurons, we have to define the neurons as m-dimensional vectors, Wj(wj1, wj2, . , wji, . , wjm). They are composed of weights being adapted during the training process. 2. Basic Architecture of Kohonen Neural Networks Basically, we differ between one- and two-dimensional organization of neurons in the single layer of the Kohonen neural network.
5 Hopfield network of the Hopfield net is updated until it converges to an equilibrium state, called the attractor state. Since any input eventually leads the network to one of the attractor states, the Hopfield net can be used as an associative memory to retrieve partially corrupted patterns. The classic traveling salesman problem can be solved with the Hopfield net. 3. Kohonen Maps The Kohonen map  is another feedback network. It has a single hidden layer. The nodes in the hidden layer are organized typically as two-dimensional rectangular grids, as shown in Fig.
After the transformation of 512 intensities, the resulting sets of 512 Hadamard coefficients were truncated to 128; hence, a one to four reduction of the original spectrum representation was achieved. The reduction reflects mainly in a lower resolution (high-frequency terms). The reduced spectra given with representation vectors Xs =(xs1, xs2, . , xsi. . , xs128) were used for model input. 2. Representation of the Chemical Structure of the Organic Compounds Studied The structure representation vectors were 34-dimensional binary vectors, Ts denoting the presence (tsj = 1) or absence (tsj = 0) of the jth structural fragment.