59 Pages

# Oblicon Provisions

Course Number: ACCT 101, Spring 2011

College/University: De La Salle University

Word Count: 16289

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Topic 1: 11561178 Art. 1156. An obligation is a juridical necessity to give, to do or not to do. (n) Art. 1157. Obligations arise from: (1) Law; (2) Contracts; (3) Quasicontracts; (4) Acts or omissions punished by law; and (5) Quasidelicts. (1089a) Art. 1158. Obligations derived from law are not presumed. Only those expressly determined in this Code or in special laws are...

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San Diego State - CSCI - 1800
Chapter 1DECISION SUPPORT SYSTEMS AND BUSINESS INTELLIGENCELearning Objectives Understand today's turbulent business environment and describe how organizations survive and even excel in such an environment Understand the need for computerized support o
Istanbul Universitesi - ECE - 501
% % %Thesis work a simulation actually first simulation% a simple regression will be done using least square support vector % machines d = [1 5;2 2;3 1;4 2]; % data points %compute kernel matrix using exponential function that is infinite 0imensional in
Istanbul Universitesi - ECE - 501
% a simple regression will be done using least square support vector % machines %clear all d2 = [1 5;1.5 3.25;2 2;2.5 1.25;3 1;3.5 1.25;4 2;4.5 3.25]; % data points %compute kernel matrix using exponential function that is infinite 0imensional inner produ
Istanbul Universitesi - ECE - 501
% Finding FIR Filter Order clear all % STEP 1 : inputs and outputs. Nt = 400; cclass = 0; for epoch = 1:30 u=normrnd(0,2,1,Nt); % A white gaussian input sequence u with length %Nt 0 mean and standard deviation 2 ut=normrnd(0,2,1,200); %input for testing.
Istanbul Universitesi - ECE - 501
% Main Simulation % clear all ic = i; Nt = 800 ;cclass = 0; % N: # of input datas, cclass : # of correct class (:p value) for epoch = 1:5 u=.2*normrnd(0,2,1,Nt); % A white gaussian input sequence u with length %Nt 0 mean and standard deviation 2 % u = ran
Istanbul Universitesi - ECE - 501
clear all u=normrnd(0,2,1,400);% A white gaussian input sequence u with length %400 0 mean and standard deviation 2 ut=normrnd(0,2,1,200); %input for testing. e=normrnd(0,.2,1,400); % A white gaussian with zero mean and standart de %viation .2 with lengt
Istanbul Universitesi - ECE - 501
clear all u=normrnd(0,2,1,500);% A white gaussian input sequence u with length %400 0 mean and standard deviation 2 %ut=normrnd(0,2,1,200); %input for testing. e=normrnd(0,.2,1,400); % A white gaussian with zero mean and standart de %viation .2 with leng
Istanbul Universitesi - ECE - 501
% % % % % %This function finds output of linear time invariant system. given input number of cycles and filter coefficents. N: # of cycles u: input to lti filter b: numerator coefficients of lti filter a: denumerator coefficients of lti filterfunction [
Istanbul Universitesi - ECE - 501
% Main Regression Simulation % clear all L = 900; % Simulation runs this much. u=1*normrnd(0,1,1,L); % A white gaussian input sequence u with length %L 0 mean and standard deviation 2 ut=normrnd(0,2,1,200); %input for testing. e=normrnd(0,.1,1,L); % A whi
Istanbul Universitesi - ECE - 501
% % % % %We will use only a filter considering it a wiener model. (that is : use outputs instead of inputs fore noise to examine colored noise effects And it seems that for extreme values of sigma the algorithm performs satisfactorily.clear all u=normrn
Istanbul Universitesi - ECE - 501
% A function that makes test of resultion support vector machine solution function [reg] = regres(x_t,sol,d) top = 0; for i = 1:4 top = top+ sol(i+1,1)*exp(-(x_t-d(i,1)^2); end reg = top + sol(1,1);
Istanbul Universitesi - ECE - 501
% A function that makes test of resultion support vector machine solution function [reg] = regres2(x_t,sol,d2) top = 0; for i = 1:8 top = top+ sol(i+1,1)*exp(-(x_t-d2(i,1)^2); end reg = top + sol(1,1);
Istanbul Universitesi - ECE - 501
% Some Tests for Identification of Wiener model. Obtaining the inverse of % the filter. that is obtain inputs from outputs. % -clear all u=normrnd(0,2,1,400); % A white gaussian input sequence u with length %400 0 mean and standard deviation 2 uback = zer
Istanbul Universitesi - ECE - 501
% now we will produce a function that computes the output of the svm % directly. that is w'*fi(x) + d. function [val] = svm_out(xtest,xtrain,bet,alph,d,sg,r) %xtrain: xtrain must be in this form. each column is a seperate training 0ata. it is assumed to b
Istanbul Universitesi - ECE - 501
% now we will produce a function that computes the output of the svm % directly. that is w'*fi(x) + d. function [val] = svm_out_reg(xtest,xtrain,alph,d,sg) %xtrain: xtrain must be in this form. each column is a seperate training 0ata. it is assumed to be
Istanbul Universitesi - ECE - 501
% % the function filter is examined. start the WienerHammersteinIdent first. filtOut = filter(b1,a1,u_test); filtIn = filter(a1,b1,filtOut); s system % actual 1st filter's output % output of whole actual
Istanbul Universitesi - ECE - 501
% Wiener identification : thsing small % signal analysis. a fear function with svm. % Then we will construct a closed loop system where at the feedback the % inverse model of the nonlinearity is present. And we may add a controller % such that the control
Istanbul Universitesi - ECE - 501
% Wiener identification : thinking it as a Hammerstein model. Using small % signal analysis. a filter and a gain is obtained. than the svm is % trained to model the overall nonlineah svm. % Then we will construct a closed loop system where at the feedback
Istanbul Universitesi - ECE - 501
% Wiener identification : thinking it as a Hammerstein model. Using small % signal analysis. here a stepwise constant is added to input. But the % results seem to be nice for d % transient time. Increasing the training data gave worse results. % Decreasin
Istanbul Universitesi - ECE - 501
% part a) in order to obtain a least squares solution obtain the matrix A %clear all N = 300; %# of training data r = 9; A = [-sim_out1(100+r:100+N-1) -sim_out1(100+r-1:100+N-2) -sim_out1(100+r-2:100+N3). -sim_out1(100+r-3:100+N-4) -sim_out1(100+r-4:100+N
Istanbul Universitesi - ECE - 501
% Wiener - Hammerstein Identification (by convolution and small signal % analysis ) clear all b1 = [1 .8 .3]; ic =i; a1 = poly([.78*exp(ic) .78*exp(-ic) .88*exp(1.6*ic) .88*exp(-1.6*ic)]); a2 = poly([.98*exp(2*ic) .98*exp(-2*ic) .96*exp(3.6*ic) .96*exp(-3
Istanbul Universitesi - ECE - 501
% Wiener system identification. % % clear all u=normrnd(0,2,1,600); % A white gaussian input sequence u with length %400 0 mean and standard deviation 2 e=normrnd(0,.2,1,600); % A white gaussian with zero mean and standart de %viation .2 with length 400.
Istanbul Universitesi - ECE - 501
% part a) in order to obtain a least squares solution obtain the matrix A. % From the simulink document WienerHammersteinConvolutionOfTwoFilters we % obtain data. Before tha % both filters. This is the question. It seems that changing the cascade % row do
Istanbul Universitesi - ECE - 501
% Nonparametric identification of wiener hammerstein system. % Obtain inputs and outputs of model clear all u=normrnd(0,2,1,500); % A white gaussian input sequence u with length %400 0 mean and standard deviation 2 ut=normrnd(0,2,1,200); %input for testin
Istanbul Universitesi - ECE - 501
% Nonparametric identification of wiener hammerstein system. % Obtain inputs and outputs of model clear all u=normrnd(0,2,1,500); % A white gaussian input sequence u with length %400 0 mean and standard deviation 2 ut=normrnd(0,2,1,200); %input for testin
Istanbul Universitesi - ECE - 501
% Nonparametric identification of wiener hammerstein system. % Obtain inputs and outputs of model clear all u=normrnd(0,2,1,500); % A white gaussian input sequence u with length %400 0 mean and standard deviation 2 ut=normrnd(0,2,1,200); %input for testin
Istanbul Universitesi - ECE - 501
% Parametric identification of wiener-hammerstein system. % Obtain inputs and outputs of model clear all u=normrnd(0,2,1,500); % A white gaussian input sequence u with length %400 0 mean and standard deviation 2 ut=normrnd(0,2,1,200); %input for testing.
Istanbul Universitesi - ECE - 501
% Wiener model identification for any type of nonlinearity: u =c + a*randn % will be used as input to the system. The results seems to be % satisfactory for denumerator parameters but not for numerator % parameters. Actually the role of inputs and outputs
Istanbul Universitesi - ECE - 501
% Wiener model identification for any type of nonlinearity: u =c + a*randn % will be used as input to the system. The results seems to be % satisfactory for denumerator parameters but not for numerator % parameters. Actually the role of inputs and outputs
Istanbul Universitesi - ECE - 501
% Kernel Matrix : Polynomial % Wiener model identification for any type of nonlinearity: u =c + a*randn % will be used as input to the system. The results seems to be % satisfactory for denumerator parameters but not for numerator % parameters. Actually t
Istanbul Universitesi - ECE - 501
% Identification of Wiener model. various breakpoints and slopes are % considered for nonlinear function. The signal used to excite the system % is not a small one. So even if the nonlinear function is invertible the % system is not identified correctly.
Istanbul Universitesi - ECE - 501
% . KERNEL IS POLYNOMIAL. % Identification of Wiener model. various breakpoints and slopes are % considered for nonlinear function. The signal used to excite the system % is not a small one. So even if the nonlinear function is invertible the % system is
Istanbul Universitesi - ECE - 501
% Identification of Wiener model. Be carefull with this simulation. Breakaway point nonlinearity is used % By using small signals the filter and a gain is estimated by changing the % role of inputs and outputs. No noise is used thats why nice results are
Istanbul Universitesi - ECE - 501
% Identification of Wiener model. % -c clear all u=normrnd(0,2,1,400); ut=normrnd(0,2,1,200); e=normrnd(0,.2,1,400); % A white gaussian input sequence u with length %400 0 mean and standard deviation 2 %input for testing. % A white gaussian with zero mean
Istanbul Universitesi - ECE - 501
% Main Regression Simulation % check for the sg value of K matrix and kernel_matrix functions. they are % changed but now they are the same. clear all u=1*normrnd(0,2,1,700); % A white gaussian input sequence u with length %700 0 mean and standard deviati
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Shereen ElahiVocabulary; SCI/162 Principles of health &amp; wellnessChapter 1: promoting healthy behavior changeHealth: the ever-changing process of achieving individual potential in the physical, social, emotional, mental, spiritual, and environmental dim
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Chapter 03 - Professional EthicsCHAPTER 3Professional EthicsReview Questions 3-1 An ethical dilemma is a situation that an individual faces involving a decision about appropriate behavior. Ethical dilemmas generally involve situations in which the welf
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CHAPTER 11 Accounts Receivable, Notes Receivable, and RevenueReview Questions 11-1 The term &quot;customer's order&quot; refers to the purchase order received from a customer. The term &quot;sales order&quot; refers to the document created upon receipt of a customer's order
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