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"Adaptive computing systems Congresses."
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Performance analysis of stirling engine using computational intelligence techniques (ANN & Fuzzy Mamdani Model) and hybrid algorithms (ANN-PSO & ANFIS)
Stirling engine is considered as one of the most promising alternatives to conventional combustion units due to its versatility and potential to achieve relatively high efficiency. The output power and torque are the main performance indicators that depend on many variables. Many studies have pointed out that the relationship between the performance indicators of the Stirling engine and its input variables was nonlinear. This study analyses the prediction performance of power and torque in a Stirling engine system using soft computing techniques—artificial neural network (ANN) and Fuzzy Mamdani Model (FMM) and hybrid algorithms—adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network trained with particle swarm optimization (ANN-PSO). The performance of these approaches has been discussed using a dataset from a test conducted on an existing Stirling engine. The performance indicators of the different models considering the power and the torque were predicted and analysed. A parametric analysis has been performed for the ANN-PSO model to identify the best model configuration considering the number of neurons in hidden layers, the number of swarm size and acceleration factors. A detailed description of the process leading to the identification of the best networks architecture for the power and torque model has been provided. The comparison of the four approaches indicates that FMM exhibits the highest performance prediction considering the power while the ANN-PSO and ANFIS model exhibit the highest performance considering the torque. This study demonstrates the suitability of soft computing techniques and hybrid algorithms for the prediction of Stirling engine characteristics and its potential to optimize time and experimental cost.
Journal Article
New Perspectives on Irish English
Ireland has experienced momentous change in the last decade and a half. Migrants now make up a significant percentage of the population and the question of integration continues to be pertinent. One indicator of integration is language, and the fluency with which an L2 speaker uses L1 discourse markers indicates how integrated he/she is into the local community (Sankoff et al. 1997). This paper analyses discourse like and its use by Polish speakers of L2 Irish English. Our research shows that speakers follow Irish English patterns, but there is a high degree of interspeaker variation. By drawing on qualitative data, we attempt to illustrate some possible reasons for this, including the potential of this feature as a tool in identity construction. Keywords: discourse like; identity; Poland; Ireland.
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