Extended Hierarchical Extreme Learning Machine with Multilayer Perceptron

Hierarchical multilayer perceptron based language identification

Automatic language identification (LID) systems generally exploit acoustic knowledge, possibly enriched by explicit language specific phonotactic or lexical constraints. This paper investigates a new LID approach based on hierarchical multilayer perceptron (MLP) classifiers, where the first layer is a “universal phoneme set MLP classifier”. The resulting (multilingual) phoneme posterior sequenc...

Hierarchical extreme learning machine based reinforcement learning for goal localization

The objective of goal localization is to find the location of goals in noisy environments. Simple actions are performed to move the agent towards the goal. The goal detector should be capable of minimizing the error between the predicted locations and the true ones. Few regions need to be processed by the agent to reduce the computational effort and increase the speed of convergence. In this pa...

Using Background Knowledge in Multilayer Perceptron Learning

In this contribution we present a method for constraining the learning of a Multi-Layer Perceptron network with background knowledge. The algorithms presented here can be used to train the partial derivatives of the network to match given numerical values or to minimize a given cost function. Thus the mapping produced by the network can be constrained according to known input-output models, mon...

Multilayer Perceptron Based Hierarchical Acoustic Modeling for Automatic Speech Recognition

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 ...