Head-Related Transfer Function Selection Using Neural Networks
نویسندگان
چکیده
منابع مشابه
rodbar dam slope stability analysis using neural networks
در این تحقیق شبکه عصبی مصنوعی برای پیش بینی مقادیر ضریب اطمینان و فاکتور ایمنی بحرانی سدهای خاکی ناهمگن ضمن در نظر گرفتن تاثیر نیروی اینرسی زلزله ارائه شده است. ورودی های مدل شامل ارتفاع سد و زاویه شیب بالا دست، ضریب زلزله، ارتفاع آب، پارامترهای مقاومتی هسته و پوسته و خروجی های آن شامل ضریب اطمینان می شود. مهمترین پارامتر مورد نظر در تحلیل پایداری شیب، بدست آوردن فاکتور ایمنی است. در این تحقیق ...
Efficient Parameters Selection for CNTFET Modelling Using Artificial Neural Networks
In this article different types of artificial neural networks (ANN) were used for CNTFET (carbon nanotube transistors) simulation. CNTFET is one of the most likely alternatives to silicon transistors due to its excellent electronic properties. In determining the accurate output drain current of CNTFET, time lapsed and accuracy of different simulation methods were compared. The training data for...
متن کاملOptimal transfer function neural networks
Abstract. Neural networks use neurons of the same type in each layer but such architecture cannot lead to data models of optimal complexity and accuracy. Networks with architectures (number of neurons, connections and type of neurons) optimized for a given problem are described here. Each neuron may implement transfer function of different type. Complexity of such networks is controlled by stat...
متن کاملApproximating the head-related transfer function using simple geometric models of the head and torso.
The head-related transfer function (HRTF) for distant sources is a complicated function of azimuth, elevation and frequency. This paper presents simple geometric models of the head and torso that provide insight into its low-frequency behavior, especially at low elevations. The head-and-torso models are obtained by adding both spherical and ellipsoidal models of the torso to a classical spheric...
متن کاملPortfolio selection using neural networks
In this paper we apply a heuristic method based on artificial neural networks in order to trace out the efficient frontier associated to the portfolio selection problem. We consider a generalization of the standard Markowitz mean-variance model which includes cardinality and bounding constraints. These constraints ensure the investment in a given number of different assets and limit the amount ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Archives of Acoustics
سال: 2017
ISSN: 2300-262X
DOI: 10.1515/aoa-2017-0038