Achmad Hidayatno

Comparison of three back-propagation architectures for interactive animal names utterance learning

English language is interesting for native speaker but there are many difficulties due to pronunciation. In order to facilitate for beginner to learn how to appropriately utter English word, we developed interactive learning program based on speech recognition. This paper investigates performance of three back-propagation neural network architectures with different hidden layers, e.g. 3, 4, and 5. The neural network is used to implements a speech recognition system to make interactive animal names utterance learning. The performance indicator that used in this study is number of epoch, training time, and mean square error (mse). The train dataset consist of 1, 2, and 3 syllables of animal names. The more hidden layer causes the longer training time but the smaller of the mse. Related to the number of epochs for training 1 and 2 syllables have a tendency that more hidden layers will be less the epoch, but this is not the case for training 3 syllables.

Error Analysis of ON-OFF and ANN Controllers Based on Evapotranspiration

The changing of climate directly affects on decreasing agricultural production. Therefore, application of automated system in agriculture activities is potential issue which must be considered. This paper presents error analysis of ON-OFF and ANN controllers which are applied to the automated irrigation system. Controlling of irrigation system used a calculated Penman-Monteith evapotranspiration and a reference of soil moisture as the compared input. Input parameters of the evapotranspiration included temperature, heat radiation, atmosphere pressure and wind speed. The performance of such controllers were evaluated and compared based on error of both controllers. The simulation result of ONOFF controller was used as the reference of controller development based on ATmega 8 microcontroller. The simulation results show that the error of the ON-OFF controller can be adjusted by setting the sampling time of the dead zone discretization. Error of the ON-OFF controller with sampling time of 0.05 second is equal to the error of ANN controller; these are 14.3% of the reference of signal amplitude.