Bidirectional associative memory network

Z. Zhang, H. Zhuo, S. Liu and P. D. B. Harrington, Classification of cancer patients based on elemental contents of serums using bidirectional associative memory networks. Anal. Chim. Acta, 436(2), 2001, 281-291. 

Two-layer feedforward/feedback ANNs are heteroassociative. They can store input and output vectors and are useful in recalling an output vector when presented with a noisy or incomplete version of its corresponding input vector. They are also useful for classification problems. Typically, every feedforward connection between two PEs is accompanied by a feedback connection between the same two PEs. Both connections have weights, and these weights are usually different from each other. Examples are the adaptive resonance theory and bidirectional associative memory networks. 

Both cases can be dealt with both by supervised and unsupervised variants of networks. The architecture and the training of supervised networks for spectra interpretation is similar to that used for calibration. The input vector consists in a set of spectral features yt(Zj) (e.g., intensities at selected wavelengths zi). The output vector contains information on the presence and absence of certain structure elements and groups fixed by learning rules (Fig. 8.24). Various types of ANN models may be used for spectra interpretation, viz mainly such as Adaptive Bidirectional Associative Memory (BAM) and Backpropagation Networks (BPN). The correlation... 

Schierle and Otto [63] used a two-layer perceptron with error back-propagation for quantitative analysis in ICP-AES. Also, Schierle et al. [64] used a simple neural network [the bidirectional associative memory (BAM)] for qualitative and semiquantitative analysis in ICP-AES. 

Some historically important artificial neural networks are Hopfield Networks, Per-ceptron Networks and Adaline Networks, while the most well-known are Backpropa-gation Artificial Neural Networks (BP-ANN), Kohonen Networks (K-ANN, or Self-Organizing Maps, SOM), Radial Basis Function Networks (RBFN), Probabilistic Neural Networks (PNN), Generalized Regression Neural Networks (GRNN), Learning Vector Quantization Networks (LVQ), and Adaptive Bidirectional Associative Memory (ABAM). 

The bidirectional associative memory (BAM) is used here to explain the operation of a neural network in more detail. 

B. Lenze, Neural Networks, 11, 1041 (1998). Complexity Preserving Increase of the Capacity of Bidirectional Associative Memories by Dilation and Translation. 

The concept of the autoassociative memory was extended to bidirectional associative memories (BAM) by Kosko (1987,1988). This memory, shown in Fig. 19.30, is able to associate pairs of the patterns a and b. This is the two-layer network with the output of the second layer connected directly to the input of the first layer. The weight matrix of the second layer is and W for the first layer. The rectangular weight matrix W is obtained as a sum of the cross-correlation matrixes... 

There is nothing like the neural network . Many types of neural networks have been proposed and used for various purposes, like Hopfield networks,the adaptive bidirectional associative memory,the Kohonen network, and radial basis funetion For the con-... 

ANN = artificial neural network AR = approximate reasoning BAM = bidirectional associative memory FAM = fuzzy associative memory FL = fuzzy logic LP = linear programming OLS = ordinary least squares PCCF = partial cross correlation function SSC = spectrum-structure correlation sup = supremum = fuzzy addition = fuzzy subtraction O = fuzzy multiplication Q = fuzzy division Q = relation. 

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