This work has considered theoretical aspects of the optimal control of synchronous motors using artificial neural networks.A brief summary of the work presented in the research and the most important conclusions that can be drawn from the results are:Optimum Performance Characteristics of Synchronous MotorsIn this work the efficiency maximization of synchronous motor is achieved by thr~e methods of control which are:i-Field Control Method:In this control method the motor is operated at maximum efficiency by variation of input field voltage with constant supply voltage (armature voltage) as the load torque change. As the load torque changes with constant armature voltage, the motor can operate at different values of field voltages, from these values of field voltages one at which minimum total power losses is achieved (i.e. maximum efficiency). The power factor at which motor operates is unity for load torque higher than 10 N.m, at these loads the field power losses is very small compared to armature power losses, this means that efficiency maximization is obtained at minimum armature current (unity power factor). For values of load torque less than 10 N.m the field power losses. cannot be neglected compared to armature power losses so unity power factor cannot be reached.2-Field-Armature Control Method:In this method of control the motor will operate at maxim_efficiency by variation of input field voltage and supply voltage (armature voltage} •• load torque change. In this method of control as the load torque changes;tM.~cy is calculated at certain field voltage and all the values of armature vol-,,,1.-,o.220 V per phase then the maximum value of efficiency is selected, the$e•eatcwations is repeated atdifferent values of field voltage to obtain the overall maximum efficiency. In this control method the same load torque is developed as the conventional case but with lower field and armature losses, which means higher efficiency than conventional operation. The efficiency of the motor under this method of control is higher than the first method at light loads .The disadvantage of this method is that the controller is more complex than the first one as two variables are controlled rather than one variable in the first method.3-Field orientation Control Method:As To. <Da <Dr sin’l’ ,for a certain load torque the angle angle between stator and rotor fluxes ’I’ can be contolled by the value of rotor flux <Dr with constant stator flux <Da to keep the ange ’I’ to be 90.In this method motor will operate’ at maximum efficiency by variation of input field voltage with constant supply voltage (armature voltage) and operation at angle between stator and rotor fluxes ’I’ =90 (field orientation) as the load torque change. In this method of control load torque cannot reach the values obtained in the two other methods of control as the maximum allowable value of excitation emf Ear should be less than the rated armature voltage Va i.e. 220V ,so load torque cannot reach the values obtained in the other two methods of control to maintain the two fluxes of armature and field winding be perpendicular to each other, this is the major disadvantage of this method of control.6.1 Conclusion• Fingerprint recognition has a good balance of all the desirable properties required for a biometric system. Fingerprints are distinctive, fingerprint details are permanent and fingerprint sensors can easily capture high quality images.• Wavelet transform can be used for fingerprint feature extraction, this is because it analyzes the image in both space and frequency domains.• Fourier transform is not a suitable feature extraction technique for fingerprints. This is because it analyzes the image in frequency domain only.• Feed-forward back propagation networks can be used for fingerprint recognition with good accuracy. They depend on minimizing the error between the output and target feature vectors.• Correlation technique can be used to solve the problem of capturing the fingerprint with different impressions. The similarity between the different impressions for the same person results in a correlation coefficient value close to one.• The distance measure classifier can be used to solve the problem of different impressions for the same person. The distance measure value is approximately zero between the different impressions for the same person.• Minutia-based methods suffer from the problem of false or missed minutia. On the other hand, wavelet transform deals with the rich gray information available in fingerprints, so it is easier and more accurate.6.2 Future WorkThere comes a stage in the development of any biometric recognition system where it becomes increasingly difficult to achieve significantly better performance from a given biometric identifier and the need to explore other sources for improvement becomes a practical necessity. This implies that for the desired performance improvement, we may need to rely on integrating multiple biometrics.