Massive parallel robots (MPRs) driven by discrete actuators are force regulated robots that undergo continuous motions despite being commanded through a finite number of states only. Designing a real-time control of such systems requires fast and efficient methods for solving their inverse static analysis (ISA), which is a challenging problem and the subject of this thesis. In particular, five Artificial intelligence methods are proposed to investigate the on-line computation and the generalization error of ISA problem of a class of MPRs featuring three-state force actuators and one degree of revolute motion.

Previous researches about electrical load time series data forecasting showed that the result was not satisfying. This paper elaborates the enhanced neuro-fuzzy architecture for the same application. The system uses Gaussian membership function (GMF) for Takagi [2] showed that the MSE is not acceptable. For this reason, the same research but with another method, i.e. enhanced Gustafson-Kessel using evolutionary algorithm has evaluated by [3], but still the result is not acceptable. The other researches [4]-[6] also show that the result is not good. Their RMSE are 5.4%. In order to reach RMSE under 5%, the research is continued with different approach.This paper elaborates the implementation of neuro-fuzzy for electrical load time series data forecasting. The proposed method uses the same data as in [1]- [6] so that the methods can be easily compared. According to those researches, the data is from East Java-Bali from September 2005 to August 2007. The neuro-fuzzy architecture will be explored in order to get the optimum architecture. It uses MIMO Takagi-Sugeno type and Levenberg-Marquardt training algorithm to make the training efficient. The architecture of neural network uses feed-forward neural network. The forecasting system uses both short and long time forecasting. Data from

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Multivariate inputs play important role in system with many dependent variables. By using some different inputs as input in neuro-fuzzy networks, complex nonlinear model can be modeled and also be forecasted with better results. This paper describes a neuro-fuzzy approach with additional fuzzy C-Means clustering method before the input entering the networks. Afterwards, the network can be used to efficiently forecast electrical load competition data using the Takagi-Sugeno (TS) type multi-input single-output (MISO) neuro-fuzzy network. The training algorithm is efficient in the sense that it can bring the performance index of the network, such as the sum squared error (SSE), down to the desired error goal much faster than the simple Levenberg-Marquardt algorithm (LMA).

The paper describes a neuro-fuzzy approach with additional moving average window data filter and fuzzy clustering algorithm that can be used to forecast electrical load using the Takagi-Sugeno (TS) type multi-input single-output (MISO) neuro-fuzzy network efficiently. The training algorithm with additional moving average filter is efficient in the sense that it can bring the performance index of the network, such as the sum squared error (SSE), down to the desired error goal much faster than the simple Levenberg-Marquardt algorithm (LMA). The fuzzy clustering algorithm allows the selection of initial parameters of fuzzy membership functions, e.g. mean and variance parameters of Gaussian membership functions of neuro-fuzzy networks, which are otherwise selected randomly. The initial parameters of fuzzy membership functions, which result in low SSE value with given training data of neuro-fuzzy network, are further fine tuned during the network training. Finally, the above training algorithm is tested on TS type MISO neuro-fuzzy structure for long-term forecasting application of electrical load time series.