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دانلود مقاله ارائه روش جديد جهت حذف نويز آكوستيكی در يك مجرا

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تعداد صفحات : 105 صفحه

قسمتی از متن :
چكيده
فصل صفر: مقدمه
1
2
فصل اول: مقدمه اي بر كنترل نويز آكوستيكي 7
1-1) مقدمه 8
1-2) علل نياز به كنترل نويزهاي صوتي (فعال و غير فعال) 9
1-2-1) بيماري هاي جسمي 9
1-2-2) بيماري هاي رواني 9
1-2-3) راندمان و كارايي افراد 9
1-2-4) فرسودگي 9
1-2-5) آسايش و راحتي 9
1-2-6 جنبه هاي اقتصادي 10
1-3) نقاط ضعف كنترل نويز به روش غيرفعال 10
1-3-1) كارايي كم در فركانس هاي پايين 10
1-3-2) حجم زياد عايق هاي صوتي 10
1-3-3) گران بودن عايق هاي صوتي 10
1-3-4) محدوديت هاي اجرايي 10
1-3-5) محدوديت هاي مكانيكي 10
1-4) نقاط قوت كنترل نويز به روش فعال 11
1-4-1) قابليت حذف نويز در يك گسترده ي فركانسي وسيع 11
1-4-2) قابليت خود تنظيمي سيستم 11
1-5) كاربرد ANC در گوشي فعال 11
1-5-1) تضعيف صدا به روش غير فعال در هدفون 12
1-5-2) تضعيف صدا به روش آنالوگ در هدفون 13
1-5-3) تضعيف صوت به روش ديجيتال در هدفون 15
1-5-4) تضعيف صوت به وسيله ي تركيب سيستم هاي آنالوگ و ديجيتال در هدفون 16
1-6) نتيجه گيري 17

فصل دوم: اصول فيلترهاي وفقي
18
2-1) مقدمه 19
2-2) فيلتر وفقي 20
2-2-1) محيط هاي كاربردي فيلترهاي وفقي 22
2-3) الگوريتم هاي وفقي 25
2-4) روش تحليلي 25
2-4-1) تابع عملكرد سيستم وفقي 26
2-4-2) گراديان يا مقادير بهينه بردار وزن 28
2-4-3) مفهوم بردارها و مقادير مشخصه R روي سطح عملكرد خطا 30
2-4-4) شرط همگرا شدن به٭ W 32
2-5) روش جستجو 32
2-5-1) الگوريتم جستجوي گردايان 32
2-5-2) پايداري و نرخ همگرايي الگوريتم 35
2-5-3) منحني يادگيري 36
2-6) MSE اضافي 36
2-7) عدم تنظيم 37
2-8) ثابت زماني 37
2-9) الگوريتم LMS 38
2-9-1) همگرايي الگوريتم LMS 39
2-10) الگوريتم هاي LMS اصلاح شده 40
2-10-1) الگوريتم LMS نرماليزه شده (NLMS) 41
2-10-2) الگوريتم هاي وو LMS علامتدار وو (SLMS) 41
2-11) نتيجه گيري 43

فصل سوم: اصول كنترل فعال نويز
44
3-1) مقدمه 45
3-2) انواع سيستم هاي كنترل نويز آكوستيكي 45
3-3) معرفي سيستم حذف فعال نويز تك كاناله 47
3-4) كنترل فعال نويز به روش پيشخور 48
3-4-1) سيستم ANC پيشخور باند پهن تك كاناله 49
3-4-2) سيستم ANC پيشخور باند باريك تك كاناله 50
3-5) سيستم هاي ANC پسخوردار تك كاناله 51
3-6) سيستم هاي ANC چند كاناله 52
3-7) الگوريتم هايي براي سيستم هاي ANC پسخوردار باند پهن 53
3-7-1) اثرات مسير ثانويه 54
3-7-2) الگوريتم FXLMS 57
3-7-3) اثرات فيدبك آكوستيكي 61
3-7-4) الگوريتم Filtered- URLMS 66
3-8) الگوريتم هاي سيستم ANC پسخوردار تك كاناله 69
3-9) نكاتي درباره ي طراحي سيستم هاي ANC تك كاناله 70
3-9-1) نرخ نمونه برداري و درجه ي فيلتر 72
3-9-2) عليت سيستم 73
3-10) نتيجه گيري 74

فصل چهارم: شبيه سازي سيستم ANC تك كاناله
75
4-1) مقدمه 76
4-2) اجراي الگوريتم FXLMS 76
4-2-1) حذف نويز باند باريك فركانس ثابت 76
4-2-2) حذف نويز باند باريك فركانس متغير 81
4-3) اجراي الگوريتم FBFXLMS 83
4-4) نتيجه گيري 85

فصل پنجم: كنترل غيرخطي نويز آكوستيكي در يك ماجرا
86
5-1) مقدمه 87
5-2) شبكه عصبي RBF 88
5-2-1) الگوريتم آموزشي در شبكه ي عصبي RBF 90
5-2-2) شبكه عصبي GRBF 93
5-3) شبكه ي TDNGRBF 94
5-4) استفاده از شبكه ي TDNGRBF در حذف فعال نويز 95
5-5) نتيجه گيري 98

فصل ششم: نتيجه گيري و پيشنهادات
99
6-1) نتيجه گيري 100
6-2) پيشنهادات 101
مراجع I
مراجع

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Abstract
In acoustic noise cancelling, Active and Passive methods are used. Inspite of Passive method, Active method can cancel or reduse noise in low frequencies. In Active method a type of adaptive filter is used. FXLMS Algorithm is know as a basic way because of good tracking in a noisy space, but can be used just in linear control problems. That means in variable frequency noise or nonlinear control systems, it diverges or doesn't work.
In this thesis, At first a kind of FXLMS Algorithm which has the ability of noise canceling in a duct at the time is introduced. Because of that an optimum adaptive step size in FXLMS Algorithm is used. Arange of optimal step size at special frequencies (200-500HZ) in a duct is calculated to it as a spline curve. The frequency of input signal with MUSIC Algorithm is guessed and optimum step size predicted from spline curve and can be put in FXLMS Algorithm to make it converge at the least time. It can be shown that general FXLMS with constant step size diverges by changing the frequency. There fore it is possible to track variable frequency by the new method of this study.

Having nonlinear properties, in Acoustic Noise canceling systems, a kind of RBF neural network (TDNGRBF) has been studied that is able to model nonlinear behaviours. Therefore it is used to cancel narrowband variable frequency noise in a duct and comparison with FXLMS Algorithm. This new method in comparison with FXLMS Algorithm has higher speed and less error, with out estimating secondary path. To cancel noise with TDNGRBF, at first a duct is studied by a GRBF neural network. Then by the use of N number of time delay from input signal, N number of networks GRBF with output linear composition, it will be possible to know nonlinear systems on – line. Coefficions used in linear compositions is optimized by NLMS Algorithm.

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