Several approaches have been introduced in literature for active noise control (ANC)systems. Since Filtered-x-Least Mean Square (FxLMS) algorithm appears to be the best choice as acontroller filter. Researchers tend to improve performance of ANC systems by enhancing andmodifying this algorithm. This paper proposes a new version of FxLMS algorithm. In many ANCapplications an online secondary path modelling method using a white noise as a training signal isrequired to ensure convergence of the system. This paper also proposes a new approach for onlinesecondary path modelling in feedfoward ANC systems. The proposed algorithm is designed in a waythat the injection of white noise is stopped at the optimum point, when the modelling accuracy issufficient. In this approach, a sudden change in secondary path during the operation makes thealgorithm to reactivate injection of the white noise to adjust the secondary path estimation. Benefitingnew version of the FxLMS algorithm and not a continual injection of white noise during systemoperation makes the proposed system more desirable, also improves the noise attenuation andconvergence rate. Comparative simulation results shown in this paper indicate effectiveness of theproposed approach.

Sediment transport as a complicated and important phenomenon has attracted a lot of researchers during the last century; however there are some formulae to evaluate sediment loads in aquatic systems. Most of them still face two major problems: firstly, lack of accuracy and secondly, involvement of many parameters which makes them more challenging.Artificial Neural Networks are known as model-free universal function approximators well suited to deal with real life engineering problems including time series predictions and parameter estimation. In this paper, sediment loads are predicted using two different types of multilayer feedforward neural networks, namely Multi-Layer perception (MLP) and Radial Basis Function (RBF). The input variables for both structures are considered to be flow discharge, mean flow depth and width, mean bed material's diameter and water surface slope and the output is sediment discharge. Some different cases have been studied. The results are promising. It has been also observed that mean square prediction errors for the developed MLP is equal to 0.0063 while the devised RBF networks produces much larger mean square errors, namely 0.01260. This indicates that the MLP-load-predictor outperforms the RBF-predictor.

This paper concerns the design of a neural state observer for nonlinear dynamic systems with noisy measurement channels and in the presence of small model errors. The proposed observer consists of three feedforward neural parts, two of which are MLP universal approximators, which are being trained off-line and the last one being a Linearly Parameterized Neural Network (LPNN), which is being updated on-line. The off-line trained parts are able to generate state estimations instantly and almost accurately, if there are not catastrophic errors in the mathematical model used. The contribution of the on-line adapting part is to compensate the remainder estimation error due to uncertain parameters and/or unmodeled dynamics. A time delay term is also added to compensate the arising differential effects in the observer. The proposed observer can learn the noise cancellation property by using noise corrupted data sets in the MLPs off-line training. Simulation results in two case studies show the high effectiveness of the proposed state observing method.

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The rainfall-runoff relationship is one of the most complex hydrological phenomena. In recent years, hydrologists have successfully applied backpropagation neural network as a tool to model various nonlinear hydrological processes because of its ability to generalize patterns in imprecise or noisy and ambiguous input and output data sets. However, the backpropagation neural network convergence rate is relatively slow and solutions can be trapped at local minima. Hence, in this study, a new evolutionary algorithm, namely, particle swarm optimization is proposed to train the feedforward neural network. This particle swarm optimization feedforward neural network is applied to model the daily rainfall-runoff relationship in Sungai Bedup Basin, Sarawak, Malaysia. The model performance is measured using the coefficient of correlation and the Nash-Sutcliffe coefficient. The input data to the model are current rainfall, antecedent rainfall and antecedent runoff, while the output is current runoff. Particle swarm optimization feedforward neural network simulated the current runoff accurately with R = 0.872 and E2 = 0.775 for the training data set and R = 0.900 and E2 = 0.807 for testing data set. Thus, it can be concluded that the particle swarm optimization feedforward neural network method can be successfully used to model the rainfall-runoff relationship in Bedup Basin and it could be to be applied to other basins.

Today, health monitoring systems for turbine engines have become a vital requirement in the aviation industry. In this paper, different fault detection methods of turbine engines are reviewed based on previous research to reveal the importance of the problem and existing challenges. The existing methods use the engine signals for diagnostics, which are heavily affected by operating conditions and disturbances. The faults effect on the performance charts of the F100-PW-220 engine is detected by neural network technique to alleviate the signal variation problem. Some common faults in this type of engine are modeled, including compressor fouling, turbine blade corrosion, and fuel injection problems. The proposed method is effective in a wide range of engine working conditions such as first moments of take-off with afterburner, take-off at 0.1 M, subsonic cruise flight at 0.8 M without afterburner in 10000, 20000, and 40000 feet altitude, supersonic cruise flight at 1.6 M with afterburner in the same altitudes. The cascade neural network with probabilistic transfer functions is used in this paper and shows satisfactory fault detection, while the required training dataset is much less than the previous works. This method facilitates the fast implementation of the system due to the small training dataset and improves the diagnostics accuracy over operational time.