###### neural network

- When working with neural networks it is essential to reduce the amount of neurons in the hidden layer, which also reduces the number of weight coefficients of the neural network as a whole. (hindawi.com)
- This course helps you understand and apply two popular artificial neural network algorithms, multi-layer perceptrons and radial basis functions. (sas.com)
- ELM is used to train single layer feed forward neural network (SLFFNN). (deepdyve.com)

###### radial basis fu

- This method based on a measure giving the contribution that a neuron have an another have been applied to multi-layer perceptron and radial basis function networks. (psu.edu)

###### neuron

- History of the Perceptron The evolution of the artificial neuron has progressed through several stages. (pearltrees.com)

###### classify

- A Multilayer perceptron used to classify blue and red points. (codeproject.com)

###### classification

- Comparison of different classification methods: decision tree models, nearest neighbour approaches, naive Bayes model and multi-layer perceptron. (psu.edu)
- However, we have tried to evaluate and compare the most common classification methods (decision trees, nearest neighbour methods, naive Bayes model and multi-layer perceptrons) according to the general requirements of context-aware systems. (psu.edu)

###### Prediction

- Experimental dataset was used to train multilayer perceptron (MLP) networks to allow for prediction of dissolution kinetics. (hindawi.com)

###### neurons

- layers (usually 3 layers), some neurons in each layer and synapses between layers. (codeproject.com)
- These are the number of neurons in hidden layers. (codeproject.com)

###### data

- The ANN used is a Multi-Layer Perceptron (MLP) trained with simulated data with one turbulent layer changing in altitude. (mdpi.com)
- An adaptive multi-tiered framework, that can be utilised for designing a context-aware cyber physical system to carry out smart data acquisition and processing, while minimising the amount of necessary human intervention is proposed and applied. (springer.com)
- Fuzzy perceptron neural networks for classifiers with numerical data and linguistic rules as inputs. (springer.com)
- Uncertainty of data, fuzzy membership functions, and multilayer perceptrons. (springer.com)

###### inputs

- Step 7: Each hidden units (Zj, j=1,…,p) sums its delta inputs from units in the layer above). (slideserve.com)

###### network

- Table 7.2: Summary of training parameters and network con gurations for the Single-Layer Perceptron (SLP) and Multi-Layer Perceptron (MLP) network. (psu.edu)
- If some value was 0, that layer will not be included in network (by default the network has three layers: one input, one hidden and one output). (codeproject.com)

###### weights

- Figure below shows a typical MLP with three layers of weights. (codeproject.com)
- The input-to-hidden layer weights are randomly generated reducing computational cost. (deepdyve.com)
- The only weights to be trained are hidden-to-output layer which is done in a single pass (without any iteration) making ELM faster than conventional ML methods. (deepdyve.com)

###### general

- In this article general Multi Layer Perceptron or Back Error Propagation is presented. (codeproject.com)

###### uses

- The sample application uses this Multi Layer Perceptron and classifies the blue and red patterns generated by user. (codeproject.com)

###### single-layer

- Table 3: Action error rates (AER) for single-layer and multi-layer HMM (lower is better). (psu.edu)
- Table3 reports the performance in terms of action error rate (AER), equivalent to the word error rate in continuous ASR, for both multi-layer HMM and the single-layer HMM methods, tested on the M4 corpus. (psu.edu)

###### understand

- The framework thus becomes simpler to understand, and amenable to improvements at each layer. (psu.edu)