To evaluate the performance, the P&O-MPPT, FL-MPPT and the proposed method are simulated by a MATLAB-SIMULINK model for a grid-connected PV system. The proposed method incorporates the advantages of the P&O-MPPT to account for slow and fast changes in solar irradiance and the reduced processing time for the FL-MPPT to address complex engineering problems when the membership functions are few. In this paper, a novel MPPT technique based on FL control and P&O algorithm is presented. However, major issues of the conventional FL-MPPT are a drift problem associated with changing irradiance and complex implementation when compared with the P&O-MPPT. Fuzzy logic (FL) is another common technique that achieves vastly improved performance for MPPT technique in terms of response speed and low fluctuation about the maximum power point. Whilst several techniques have been designed, Perturb and Observe (P&O) is widely used for MPPT due to its low cost and simple implementation. Maximum power point tracking (MPPT) techniques are considered a crucial part in photovoltaic system design to maximise the output power of a photovoltaic array. This work is limited to analysis and simulation only and could be extended to a practical realization in future work. It is seen that ANFIS based system performance is better even when the parameters of the machines vary with time. The performances of these three drives are analyzed, and results are compared. MRAS, ANN, ANFIS estimators adaptive in nature so these estimators can adapt if there is any parameters change online. In this paper a MATLAB study of MRAS, ANN and ANFIS based position estimator in a Field oriented control of a permanent magnet synchronous motor drive is being done. SMO, MRAS are much discussed in literature but the artificial neural network, adaptive neuro-fuzzy inference based estimators are least discussed. Adaptive position and speed estimators viz. Adaptive position estimators are required as the parameters of the machines like rotor resistance, inductance changes sometimes. This paper deals with MATLAB/SIMULINK simulation and analysis of a position sensor-less field oriented control of permanent magnet synchronous motor. Based on 1,933,535 items of hospitalization information from more than 8000 prostate cancer cases in China, experimental results demonstrate that intelligent medical system could be adopted to assist physicians and medical specialists in coming up with a more dependable diagnosis scheme. Consequently, to address these existing problems in the current healthcare field of developing countries, this paper proposes a novel big medical data decision-making model exploiting fuzzy inference logic for prostate cancer in developing countries, constructing a machine-assisted system for disease detection, medical data analysis and fusion, treatment recommendations, and risk management. Rapid improvements in artificial intelligence, computing power, parallel operation, and data storage management have also contributed significantly to a reasonable medical decision-making on the detection, diagnosis, treatment, and prognosis of malignant diseases. Intelligent medical system, a novel applied technique with its foundation in machine learning, is emerging in recent years as an auxiliary diagnosis application for medical information fusion, promising to ameliorate the current healthcare situation of developing countries. From the perspective of social development, this unbalanced healthcare system in developing counties has also exacerbated the contradiction between physicians and patients, particularly those suffering from malignant diseases (like prostate cancer). In particular, the repeatability and complexity of medical diagnosis program, a massive influx of multimodal medical data, and behindhand medical equipment will lead to a high misdiagnosis rate and relatively low diagnosis efficiency of medical staff eventually. In most developing countries, it has become a severe challenge that limited medical resources and outdated healthcare technology cannot meet the high demand of large population.
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