توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱The Best Variable Step–size LMS Algorithm for Smart Antennas in a SDMA Mobile Communication Environment with Changing Noise and fading
اطلاعات انتشار: همایش ملی مهندسی برق و توسعه پایدار با محوریت دستاوردهای نوین در مهندسی برق، سال
تعداد صفحات: ۶
The purpose of this paper is to study the performance of variable step–size LMS algorithms for smart antennas with an application in SDMA wireless communication systems where changing noise and fading are troublesome. We will find the best variable step–size algorithm for dealing with these conditions and change it in a way to slightly improve its performance. Proposed algorithm gives better results for beam forming in desired direction and provides deeper nulls in the direction of interference signals. Simulations are made to illustrate the beam forming for various conditions including different array space factors, changing noise and fading.<\div>

۲Short–term load forecasting of Urmia city with hybrid k–means, VSS LMS” learning method for RBF neural network
اطلاعات انتشار: کنفرانس بین المللی فناوری و مدیریت انرژی، سال
تعداد صفحات: ۵
in this paper we investigate the performance of a hybrid learning algorithm for RBF network in the application of short–term load forecasting. In this method the algorithm forfinding radial basis function centers of hidden layer is k–means and the algorithm for training the weights of output layer isadaptive variable step–size algorithm. We proved this method isboth accurate and fast in comparison with other presented schemes. Also we demonstrated that this method requires lesscomputational processing and can perform well when amount of the input data is large. Our simulation results for Urmia city – Iran, show there is up to 30 percent improvement in processing time and 37% improvement in prediction accuracy whencompared with previously improved k–means learning<\div>
نمایش نتایج ۱ تا ۲ از میان ۲ نتیجه