High-order and multilayer perceptron initialization

Interactive initialization of the multilayer perceptron

A new multilayer preceptor initialization method is proposed and compared experimentally with a traditional random initialization method. An operator maps training-set vectors into a two-variate space, inspects bi-variate training-set vectors and controls the complexity of the decision boundary. Simulations with sixteen real-world pattern classi®cation tasks have shown that in small-scale patte...

Maximum covariance method for weight initialization of multilayer perceptron network

High Performance Multilayer Perceptron on a CustomComputing

Multilayer perceptrons (MLPs) are one of the most popular neural network models for solving pattern classiication and image classiication problems. Because of their ability to learn complex decision boundaries, MLPs are used in many practical computer vision applications involving classiication (or supervised segmentation). Once the connection weights in a MLP have been learnt, the network can ...

Multilayer Perceptron Training

In this contribution we present an algorithm for using possibly inaccurate knowledge of model derivatives as a part of the training data for a multilayer perceptron network (MLP). In many practical process control problems there are many well-known rules about the eeect of control variables to the target variables. With the presented algorithm the basically data driven neural network model can ...

Multilayer Perceptron Algebra

Artificial Neural Networks(ANN) has been phenomenally successful on various pattern recognition tasks. However, the design of neural networks rely heavily on the experience and intuitions of individual developers. In this article, the author introduces a mathematical structure called MLP algebra on the set of all Multilayer Perceptron Neural Networks(MLP), which can serve as a guiding principle...