• Nenhum resultado encontrado

HALF OF THRESHOLD ALGORITHM: AN ENHANCED LINEAR ADAPTIVE SKIPPING TRAINING ALGORITHM OR MULTILAYER FEEDFORWARD NEURAL NETWORKS

N/A
N/A
Protected

Academic year: 2017

Share "HALF OF THRESHOLD ALGORITHM: AN ENHANCED LINEAR ADAPTIVE SKIPPING TRAINING ALGORITHM OR MULTILAYER FEEDFORWARD NEURAL NETWORKS"

Copied!
9
0
0

Texto

Loading

Imagem

Fig. 1. HOT algorithm incorporated in MFNN architecture Step 7:  Output Layer Activation net value
Fig. 2. Epoch-wise training input samples for all datasets
Fig. 4. Total Training samples taken by BPN, LAST and HOT algorithm during the training phase
Table 3. Result comparison for the waveform dataset

Referências

Documentos relacionados

This paper aims at proposing a new Lasso algorithm for variable selection in Cox model as a non- linear extension of the Forward stagewise linear regression algorithm Tibshirani et

consistently expressing more GSTT1 relatively to β-Actin than males. However, in the single episode group, the opposite occurs. The last comparison done for GSTT1

Todo este trabalho culminou na criação duma clutch inspirada no traje típico de Nisa mas ao mesmo tempo, tende a construir a possibilidade de, futuramente, criar esta e outras peças

The present study aimed to examine the effects of using static or dynamic stretching added to the common warm-up routine for short sprint distances and to repeated

(2015) investigated combined hot air/infrared drying process and reported that feed forward back propagation neural network with topology of 4-8-14-1, training algorithm

The Enhanced Reactive Dynamic Source Routing Algorithm Applied to Mobile Ad Hoc Networks (ERDSR) protocol is another variant of DSR algorithm that chooses the

We solve a Riemann-Hilbert problem with almost periodic coefficient G, associated to a Toeplitz operator T G in a class which is closely connected to finite interval

v^ (e^) onde argmax denota a maximização de um conjunto de diversos máximos possíveis. Uma das vantagens desta estratégia é a de que existe independência entre a escolha do