f(x, y, t)の2階微分を計算できればf(x, y, t+
x () g(x) = f(t) dt f(x), F (x) 3x () g(x) g (x) f(x), F (x) (3) h(x) = x 3x tf(t) dt.9 = {(x, y) ; x, y, x + y } f(x, y) = xy( x y). h (x) f(x), F (x
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1 1.1 / Fik Γ= D n x / Newton Γ= µ vx y / Fouie Q = κ T x 1. fx, tdx t x x + dx f t = D f x 1 fx, t = 1 exp x 4πDt 4Dt lim fx, t =δx 3 t + dxfx, t = 1
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y = f(x) (x, y : ) w = f(z) (w, z : ) df(x) df(z), f(x)dx dx dz f(z)dz : e iωt = cos(ωt) + i sin(ωt) [ ] : y = f(t) f(ω) = 1 2π f(t)e iωt d
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8. 自由曲線と曲面の概要 陽関数 陰関数 f x f x x y y y f f x y z g x y z パラメータ表現された 次元曲線 パラメータ表現は xyx 毎のパラメータによる陽関数表現 形状普遍性 座標独立性 曲線上の点を直接に計算可能 多価の曲線も表現可能 gx 低次の多項式は 計
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遺傳演算法實例 f(x,y)=x^2+y^2求極大值ppt 最新協作平台活動 衛道中學程式設計 遺傳演算法實例 f(x,y)=x
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( ) x y f(x, y) = ax
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B 38 1 (x, y), (x, y, z) (x 1, x 2 ) (x 1, x 2, x 3 ) 2 : x 2 + y 2 = 1. (parameter) x = cos t, y = sin t. y = f(x) r(t) = (x(t), y(t), z(t)), a t b.
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f(x) x = A = h f( + h) f() h A (differentil coefficient) f(x) f () y = f(x) y = f( + h) f(), x = h dy dx f () f (derivtive) (differentition) * t (velo
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86 6 r (6) y y d y = y 3 (64) y r y r y r ϕ(x, y, y,, y r ) n dy = f(x, y) (6) 6 Lipschitz 6 dy = y x c R y(x) y(x) = c exp(x) x x = x y(x ) = y (init
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Fortran90/95 2. (p 74) f g h x y z f x h x = f x + g x h y = f y + g y h z = f z + g z f x f y f y f h = f + g Fortran 1 3 a b c c(1) = a(1) + b(1) c(
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O f(x) x = A = lim h f( + h) f() h A (differentil coefficient) f f () y = f(x) y = f( + h) f(), x = h dy dx f () f (derivtive) (differentition) * t (v
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y = f(x) y = f( + h) f(), x = h dy dx f () f (derivtive) (differentition) (velocity) p(t) =(x(t),y(t),z(t)) ( dp dx dt = dt, dy dt, dz ) dt f () > f x
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x(t) + t f(t, x) = x(t) + x (t) t x t Tayler x(t + t) = x(t) + x (t) t + 1 2! x (t) t ! x (t) t 3 + (15) Eular x t Teyler 1 Eular 2 Runge-Kutta
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Optical Flow t t + δt 1 Motion Field 3 3 1) 2) 3) Lucas-Kanade 4) 1 t (x, y) I(x, y, t)
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t, x (4) 3 u(t, x) + 6u(t, x) u(t, x) + u(t, x) = 0 t x x3 ( u x = u x (4) u t + 6uu x + u xxx = 0 ) ( ): ( ) (2) Riccati ( ) ( ) ( ) 2 (1) : f
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1 2 1 No p. 111 p , 4, 2, f (x, y) = x2 y x 4 + y. 2 (1) y = mx (x, y) (0, 0) f (x, y). m. (2) y = ax 2 (x, y) (0, 0) f (x,
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Kalman ( ) 1) (Kalman filter) ( ) t y 0,, y t x ˆx 3) 10) t x Y [y 0,, y ] ) x ( > ) ˆx (prediction) ) x ( ) ˆx (filtering) )
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,.,. 2, R 2, ( )., I R. c : I R 2, : (1) c C -, (2) t I, c (t) (0, 0). c(i). c (t)., c(t) = (x(t), y(t)) c (t) = (x (t), y (t)) : (1)
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y = x 4 y = x 8 3 y = x 4 y = x 3. 4 f(x) = x y = f(x) 4 x =,, 3, 4, 5 5 f(x) f() = f() = 3 f(3) = 3 4 f(4) = 4 *3 S S = f() + f() + f(3) + f(4) () *4
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II (10 4 ) 1. p (x, y) (a, b) ε(x, y; a, b) 0 f (x, y) f (a, b) A, B (6.5) y = b f (x, b) f (a, b) x a = A + ε(x, b; a, b) x a 2 x a 0 A = f x (
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