python人工智能程序代写、代写python、代写代做python程序设计

日期：2018-06-26 04:30

# -*- coding: utf-8 -*-

# -*- coding: utf-8 -*-

"""

Created on Mon Jun 25 20:45:24 2018

@author: Administrator

"""

#只能自己泰勒展开了

import math

import numpy

import pylab

#这里是假设A=1，H=1的情况

def ftx(x): #  xk的一阶导数

   f=1/2+25*(1-x*2)/((1+x^2)^2)

   return f

def fty(x):  #  yk的一阶倒数

   f=x/10

   return f

def fx(x,k): #  预测公式 无噪声

   f=0.5*x+25*x/(1+x^2)

   return f

def fy(y):    #  观测公式 无噪声

   f=(x^2)/20

   return f

# intial parameters

n_iter = 50

sz = (n_iter,) # size of array  步长

x = -0.37727 # truth value  (typo in example at top of p. 13 calls this z)

z = numpy.zeros(sz)

#  测量值  observations (normal about x, sigma=0.1)  

Q = numpy.random.normal(0,10,size=sz) # process variance #产生过程噪声

R = numpy.random.normal(0,1,size=sz) # estimate of measurement variance, change to see effect

                                    #产生的观测噪声

# allocate space for arrays

xhat=numpy.zeros(sz)      # a posteri estimate of x    X的后验估计（最优值）

P=numpy.zeros(sz)         # a posteri error estimate   X的最优质的误差估计 Pt

xhatminus=numpy.zeros(sz) # a priori estimate of x     X的先验估计（状态值）

Pminus=numpy.zeros(sz)    # a priori error estimate    X的先验估计Pt-

K=numpy.zeros(sz)         # gain or blending factor    卡尔曼滤波增益

# intial guesses

xhat[0] = 0.0

P[0] = 1.0

for k in range(1,n_iter):

   # time update

   xhatminus[k] = ftx(xhat[k-1])*xhat[k-1]+8*math.cos(1.2(k-1))     #X(k|k-1) = AX(k-1|k-1) + BU(k) + W(k),A=1,BU(k) = 0

   Pminus[k] = ftx(xhat[k-1])*P[k-1]*ftx(xhat[k-1])*+Q[k]       #P(k|k-1) = AP(k-1|k-1)A' + Q(k) ,A=1

   # measurement update

   K[k] = Pminus[k]*(fty(xhat[k-1])/(fty(xhat[k-1])* Pminus[k]*(fty(xhat[k-1])+R[k] ) #Kg(k)=P(k|k-1)H'/[HP(k|k-1)H' + R(k)],H=1