This paper proposes a hybrid modified differential evolution plus back-propagation (MDE-BP) algorithm to optimize the weights of the neural network model. In implementing the proposed training algorithm, the mutation phase of the differential evolution (DE) is modified by combining two mutation strategies rand/1 and best/1 to create trial vectors instead of only using one mutation operator or rand/1 or best/1 as the standard DE. The modification aims to balance the global exploration and local exploitation capacities of the algorithm in order to find potential global optimum solutions. Then the local searching ability of the back-propagation (BP) algorithm is applied in that region so as to swiftly converge to the optimum solution. The performance and efficiency of the proposed method is tested by identifying some benchmark nonlinear systems and modeling the shape memory alloy actuator. The proposed training algorithm is compared with the other algorithms, such as the traditional DE and BP algorithm. As a result, the proposed method can improve the accuracy of the identification process.

Water clarity is the most common indicator of water quality. The purpose of the study was to develop an instrument which can automatically measure water clarity in place of manual measurement by Secchi disk. The instrument is suspended by buoys at the water surface and uses solar energy to measure the light intensity of LED bulbs after passing through a water column; the result is then converted to Secchi depth by using a regression function. Measurement data are stored in a cloud server so that mobile users can access via an Internet connection. Three experiments were conducted to examine the instrument performance: (i) to ensure light intensity of the LED bulbs is strong enough to pass through the water column; (ii) to determine the regression relationship between the measured light intensity of the instrument and Secchi depth; and (iii) to evaluate the coefficient of variation (CV) of the measured water clarity when using our instrument and a conventional Secchi disk. Experiment results show that the measured values of light intensity are stable with the average CV = 5.25%. Moreover, although there are slight differences between the Secchi depth measured by our instrument and those measured by Secchi disk, the measurements by our instrument can efficiently replace the measurements by conventional Secchi disk, which can be affected by weather conditions as well as by human subjectivity.

In this paper, the virtual holonomic constraint approach is initiatively applied for the trajectory planning and control design of a typical double link underactuated mechanical system, called the Pendubot. The goal is to create synchronous oscillations of both links. After modeling the system using Euler-Lagrangian equations of motion, the parameters of the model are identified with optimization techniques. Using this model, the trajectory planning is done via Virtual Holonomic Constraint approach on the basis of re-parameterization of the motion according to geometrical relations among the generalized coordinates of the system.