% GSSM_LTI1  Generalized state space model for simple LTI system%% The model is a simple 2nd order LTI system with Gaussian observation noise%   Copyright (c) Oregon Health & Science University (2006)%%   This file is part of the ReBEL Toolkit. The ReBEL Toolkit is available free for%   academic use only (see included license file) and can be obtained from%   http://choosh.csee.ogi.edu/rebel/.  Businesses wishing to obtain a copy of the%   software should contact rebel@csee.ogi.edu for commercial licensing information.%%   See LICENSE (which should be part of the main toolkit distribution) for more%   detail.%===============================================================================================function [varargout] = model_interface(func, varargin)  switch func    %--- Initialize GSSM data structure --------------------------------------------------------    case 'init'      model = init(varargin);        error(consistent(model,'gssm'));               % check consistentency of initialized model      varargout{1} = model;    %--------------------------------------------------------------------------------------------    otherwise      error(['Function ''' func ''' not supported.']);  end%===============================================================================================function model = init(init_args)  model.type = 'gssm';                       % object type = generalized state space model  model.tag  = 'GSSM_LTI1';                  % ID tag  model.ffun       = @ffun;                  % file handle to FFUN  model.hfun       = @hfun;                  % file handle to HFUN  model.prior      = @prior;                 % file handle to PRIOR  model.likelihood = @likelihood;            % file handle to LIKELIHOOD  model.linearize  = @linearize;             % file handle to LINEARIZE  model.setparams  = @setparams;             % file handle to SETPARAMS  model.statedim   = 2;                      %   state dimension  model.obsdim     = 1;                      %   observation dimension  model.paramdim   = 2;                      %   parameter dimension  model.U1dim      = 0;                      %   exogenous control input 1 dimension  model.U2dim      = 0;                      %   exogenous control input 2 dimension  model.Vdim       = 1;                      %   process noise dimension  model.Ndim       = 1;                      %   observation noise dimension  Arg.type = 'gaussian';  Arg.cov_type = 'full';  Arg.dim = model.Vdim;  Arg.mu = 0;  Arg.cov  = 0.001;  model.pNoise     = gennoiseds(Arg);   % process noise : zero mean white Gaussian noise , cov=0.001  Arg.type = 'gaussian';  Arg.cov_type = 'full';  Arg.dim = model.Ndim;  Arg.mu = 0;  Arg.cov  = 0.3;  model.oNoise     = gennoiseds(Arg);     % observation noise : zero mean white Gaussian noise, cov=0.2  model.params     = zeros(model.paramdim,1);  model.A  = [model.params(1) model.params(2); 1 0];  model.B  = [];  model.C  = [1 0];  model.D  = [];  model.G  = [1 0]';  model.H  = [1];  model = setparams(model,[1.9223 -0.9604]);   % 2nd order under-damped LTI system%===============================================================================================function model = setparams(model, params, index_vector)  if (nargin==2)    model.params = params(:);  elseif (nargin==3)    model.params(index_vector) = params(:);  else    error('[ setparams ] Incorrect number of input arguments.');  end  model.A(1,:)  = model.params';%===============================================================================================function new_state = ffun(model, state, V, U1)  new_state      = model.A * state;  if ~isempty(V)      new_state(1,:) = new_state(1,:) + V(1,:);  end%===============================================================================================function tranprior = prior(model, nextstate, state, U1, pNoiseDS)  X = nextstate - ffun(model, state, [], U1);  tranprior = pNoiseDS.likelihood( pNoiseDS, X(1,:));%===============================================================================================function observ = hfun(model, state, N, U2)  observ = state(1,:);  if ~isempty(N)    observ = state(1,:) + N(1,:);  end%===============================================================================================function llh = likelihood(model, obs, state, U2, oNoiseDS)  X = obs - hfun(model, state, [], U2);  llh = oNoiseDS.likelihood( oNoiseDS, X);%===============================================================================================function out = linearize(model, state, V, N, U1, U2, term, index_vector)  if (nargin<7)    error('[ linearize ] Not enough input arguments!');  end  %--------------------------------------------------------------------------------------  switch (term)    case 'A'      %%%========================================================      %%%             Calculate A = df/dstate      %%%========================================================      out = model.A;    case 'B'      %%%========================================================      %%%             Calculate B = df/dU1      %%%========================================================      out = model.B;    case 'C'      %%%========================================================      %%%             Calculate C = dh/dx      %%%========================================================      out = model.C;    case 'D'      %%%========================================================      %%%             Calculate D = dh/dU2      %%%========================================================      out = model.D;    case 'G'      %%%========================================================      %%%             Calculate G = df/dv      %%%========================================================      out = model.G;    case 'H'      %%%========================================================      %%%             Calculate H = dh/dn      %%%========================================================      out = model.H;    case 'JFW'      %%%========================================================      %%%             Calculate  = dffun/dparameters      %%%========================================================      out = [state(1) state(2); 0 0];    case 'JHW'      %%%========================================================      %%%             Calculate  = dhfun/dparameters      %%%========================================================      out = zeros(model.obsdim,model.paramdim);    otherwise      error('[ linearize ] Invalid model term requested!');  end  if (nargin==8), out = out(:,index_vector); end  %--------------------------------------------------------------------------------------