Browsing by Author "Saad Saoud, Lyes"

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    Cognitive Quaternion Valued Neural Network and some applications
    (Elsevier, 2016) Saad Saoud, Lyes; Ghorbanib, Reza; Rahmounea, Fayçal
    A Meta-cognitive Quaternion Valued Neural Network (Mc-QVNN) learning algorithm and its forecasting applications is proposed in this paper. The Mc-QVNN has two parts, the cognitive part that contains the QVNN and a meta-cognitive part, which self-regulates the learning algorithm. At each epoch, when the Mc-QVNN receives a new sample, the meta-cognitive part makes a decision about the manner, the time and the need to learn this sample or not. In this case, the algorithm deletes the unneeded samples and keeps just the necessary ones for learning. The meta-cognitive component makes the decision according to the quaternion magnitude and phases. Three forecasting problems, which are Mackey–Glass time series, Lorenz attractor and the real home's power in the city of Honolulu in Hawaii, USA, are taken to test the performance of the proposed algorithm. Comparison with other existing methods shows that the Mc-QVNN is promising for forecasting chaotic systems
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    Generalized dynamical fuzzy model for identification and prediction
    (2014) Saad Saoud, Lyes; Rahmoune, Fayçal; Tourtchine, Victor; Baddari, Kamel
    In this paper, the development of an improved Takagi Sugeno (TS) fuzzy model for identification and chaotic time series prediction of nonlinear dynamical systems is proposed. This model combines the advantages of fuzzy systems and Infinite Impulse Response (IIR) filters, which are autoregressive moving average models, to create internal dynamics with just the control input. The structure of Fuzzy Infinite Impulse Response (FIIR) is presented, and its learning algorithm is described. In the proposed model, the Butterworth analogue prototype filters are estimated using the obtained membership functions. Based on the founding orders of the analogue filters, the IIR filters could be constructed. The IIR filters are introduced to each TS fuzzy rule which produces local dynamics. Gustafson-Kessel (GK) clustering algorithm is used to generate the clusters which will be used to find the number of the IIR parameters for each rule. The hybrid genetic algorithm and simplex method are used to identify the consequence parameters. The stability of the obtained model is studied. To demonstrate the performance of this modeling method, three examples have been chosen. Comparative results between the FIIR model on one hand, and the traditional TS fuzzy model, the neural networks and the neuro-fuzzy network on the other hand. The results show that the proposed method provides promising identification results
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    Load peak shaving and power smoothing of a distribution grid with high renewable energy penetration
    (Elsevier, 2016) Reihani, E.; Motalleb, M.; Ghorbani, R.; Saad Saoud, Lyes
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    Rainfall–runoff modelling using octonion-valued neural networks
    (Taylor & Francis, 2021) Shishegar, Shadab; Ghorbani, Reza; Saad Saoud, Lyes; Duchesne, Sophie; Pelletier, Geneviève
    Rainfall–runoff modelling is at the core of any hydrological forecasting system. The high spatio-temporal variability of precipitation patterns, complexity of the physical processes, and large quantity of parameters required to characterize a watershed make the prediction of runoff rates quite difficult. In this study, a hyper-complex artificial neural network in the form of an octonion-valued neural network (OVNN) is proposed to estimate runoff rates. Evaluation of the proposed model is performed using a rainfall time series from a raingauge near a Canadian watershed. Results of the artificial intelligence-generated runoff rates illustrate its capacity to produce more computationally efficient runoff rates compared to those obtained using a physically based model. In addition, training the data using the proposed OVNN vs. a real-valued neural network shows less space complexity (1*3*1 vs. 8*10*8, respectively) and more accurate results (0.10% vs. 0.95%, respectively), which accounts for the efficiency of the OVNN model for real-time control applications