In the area of artificial neural networks, the Back Propagation (BP) learning algorithm has proved to be efficient in many engineering applications especially in pattern recognition, signal processing and system control. Although the BP learning has been a significant research area of neural network, it has also been known as an algorithm with a poor convergence rate. Many attempts have been made on the learning algorithm to improve the performance on convergence speed and learning efficiency. In this study, we propose a new modified BP learning algorithm by adding chaotic noise into weight update process during error propagation. The chaotic noise is generated using various chaotic maps such as Logistic map, Skew Tent map and Bernoulli Shift map. By computer simulations, we confirm that our proposed algorithm can give a better convergence rate and can find a good solution in early time compared to the conventional BP learning algorithm. Weight update position, noise amplitude and control parameter of chaos can give a big effect on the learning ability of feed forward neural network.

In food process industries, sometimes there was food contamination even when it is still in process. Same as other industries when involved with liquid, product contamination which caused by bacteria can affect the quality and company reputation itself. Therefore, the bacteria growth monitoring system which has fast response and reliability is important in helping the industries control process. In this study, a method of monitoring bacteria growth using ultrasonic sensor has been developed based on measuring frequency and impedance measurement of the liquid medium. In developing a fully functional transducer that can be used directly, a transmitter and receiver circuit for the sensor, and the container to place the bacteria was designed. The transducer proves able to differentiate the change in viscosity and able to be use in different temperature.

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