def buildExperiment(ind):
net=nef.Network('EA designed recurrent SNN')
net.add_to_nengo();
# generator
# function .1 base freq, max freq 10 rad/s, and RMS of .5; 12 is a seed
generator=FunctionInput('generator',[FourierFunction(.1, 5,.3, 12),
FourierFunction(.5, 7, .7, 112)],Units.UNK)
net.add(generator);
# model
model= buildModel(ind);
net.add(model.network);
# plant
plant = net.add(modules.OR('Fuzzy OR'));
# error estimation
err = net.make('error',1,1,mode='direct');
mse = net.add(modules.mse('MSE'));
# wiring
modelIn = model.get('inputs'); # get the input model
model.network.exposeTermination(modelIn.getTermination('in'),'subInput'); # expose its termination
net.connect(generator,model.network.getTermination('subInput'),weight=1) # generator to termination
net.connect(generator,plant.getTermination('inputs')) # generator to plant
modelOut = model.get('outputt');
model.network.exposeOrigin(modelOut.getOrigin('out'),'subOutput'); # expose its termination
net.connect(model.network.getOrigin('subOutput'), err, weight=-1)
net.connect(plant.getOrigin('output'),err)
net.connect(err,mse.getTermination('error'))
return net;
def buildExperiment(ind):
net=nef.Network('EA designed recurrent SNN')
net.add_to_nengo();
# generator
# function .1 base freq, max freq 10 rad/s, and RMS of .5; 12 is a seed
generator=FunctionInput('generator',[FourierFunction(.1, 5,.3, 12),
FourierFunction(.5, 7, .7, 112)],Units.UNK)
net.add(generator);
# model
model= buildModel(ind);
net.add(model.network);
# plant
plant = net.add(modules.OR('Fuzzy OR'));
#plant = net.add(mathNodes.SUMABS('SUM'));
# error estimation
err = net.make('error',1,1,mode='direct');
mse = net.add(modules.mse('MSE'));
# wiring
modelIn = model.get('inputs'); # get the input model
model.network.exposeTermination(modelIn.getTermination('in'),'subInput'); # expose its termination
net.connect(generator,model.network.getTermination('subInput'),weight=1) # generator to termination
net.connect(generator,plant.getTermination('inputs')) # generator to plant
modelOut = model.get('outputt');
model.network.exposeOrigin(modelOut.getOrigin('out'),'subOutput'); # expose its termination
net.connect(model.network.getOrigin('subOutput'), err, weight=-1)
net.connect(plant.getOrigin('output'),err)
net.connect(err,mse.getTermination('error'))
return net;
def buildExperiment(w):
# PARENT NETWORK
net=nef.Network('SimpleModule tries to approximate the Plant')
net.add_to_nengo();
# SIGNAL GENERATOR
# function .1 base freq, max freq 10 rad/s, and RMS of .5; 12 is a seed
generator=FunctionInput('generator',[FourierFunction(.1, 5,.3, 12),
FourierFunction(.5, 7, .7, 112)],Units.UNK)
net.add(generator);
# PLANT
# can be implemented by anything (external process, signal generator, dataset..)
plant = net.add(modules.OR('Plant - Fuzzy OR'));
# MODEL
model=net.add(Model('model',2,1,3)) # system with no. of inputs,outputs,pars.
const=net.make_input('Constant',[0.2,0.4,0.6]) # set the parameters of the model
net.connect(const,model.getTermination('params'));
# ERROR estimation
err = net.make('error',1,1,mode='direct');
mmse = net.add(modules.mse('MSE'));
# WIRING it together
net.connect(generator,model.getTermination('inputs')) # generator to model
net.connect(generator,plant.getTermination('inputs')) # generator to plant
net.connect(model.getOrigin('outputs'), err, weight=-1) # model to error (negative value)
net.connect(plant.getOrigin('output'), err, weight=+1) # plant to error
net.connect(err,mmse.getTermination('error')) # actual error to MSE
return net;
def buildExperiment(ind):
net = nef.Network("EA designed recurrent HNN")
net.add_to_nengo()
# generator
# function .1 base freq, max freq 10 rad/s, and RMS of .5; 12 is a seed
generator = FunctionInput(
"generator", [FourierFunction(0.1, 5, 0.3, 12), FourierFunction(0.5, 7, 0.7, 112)], Units.UNK
)
net.add(generator)
# model
model = buildNeurons(net, ind.getMatrix().getN())
net.add(model.network)
# plant
plant = net.add(modules.OR("Fuzzy OR"))
# plant = net.add(mathNodes.SUMABS('SUM'));
# error estimation
err = net.make("error", 1, 1, mode="direct")
mse = net.add(modules.mse("MSE"))
# wiring
modelIn = model.get("inputs")
# get the input model
model.network.exposeTermination(modelIn.getTermination("in"), "subInput")
# expose its termination
net.connect(generator, model.network.getTermination("subInput"), weight=1) # generator to termination
net.connect(generator, plant.getTermination("inputs")) # generator to plant
#
modelOut = model.get("outputt")
model.network.exposeOrigin(modelOut.getOrigin("out"), "subOutput")
# expose its termination
net.connect(model.network.getOrigin("subOutput"), err, weight=-1)
#
net.connect(plant.getOrigin("output"), err)
net.connect(err, mse.getTermination("error"))
return net
def buildExperiment(ind):
net=nef.Network('IO neuron transfer fcn test')
net.add_to_nengo();
# generator
# function .1 base freq, max freq 10 rad/s, and RMS of .5; 12 is a seed
generator=FunctionInput('generator',[FourierFunction(.1, 12,.3, 12),
FourierFunction(.5, 20, .7, 112)],Units.UNK)
net.add(generator);
# model
model= buildModel(ind);
net.add(model.network);
# plant
plant = net.add(modules.OR('Fuzzy OR'));
# error estimation
err = net.make('error',1,1,mode='direct');
mse = net.add(modules.mse('MSE'));
# wiring
net.connect(generator,'model.input',weight=1) # feed the signal into plant and model
net.connect(generator,plant.getTermination('inputs'))
net.connect('model.output',err,weight=-1)
net.connect(plant.getOrigin('output'),err) # compute difference
net.connect(err,mse.getTermination('error'))
return net;
def buildExperiment(w):
# PARENT NETWORK
net=nef.Network('Simple Model tries to approximate the Plant')
net.add_to_nengo();
# SIGNAL GENERATOR
# function .1 base freq, max freq 10 rad/s, and RMS of .5; 12 is a seed
generator=FunctionInput('generator',[FourierFunction(.1, 5,.3, 12),
FourierFunction(.5, 7, .7, 112)],Units.UNK)
net.add(generator);
# PLANT
# can be implemented by anything (external process, signal generator, dataset..)
plant = net.add(modules.OR('Plant - Fuzzy OR'));
# MODEL
model = net.add(modules.exampleDynamicSystem('Model - dynamic system'));
# TODO: setup of parameter should work as weighted constant, as follows:
#const=net.make_input('Constant',[10]) # Create a constant function
#net.connect(const,model.getTermination('par_alpha'),weight=w);
const=net.make_input('Constant',[w*10]) # ..This is temporary solution
net.connect(const,model.getTermination('par_alpha'));
# ERROR estimation
err = net.make('error',1,1,mode='direct');
mmse = net.add(modules.mse('MSE'));
# WIRING it together
net.connect(generator,model.getTermination('inputs')) # generator to model
net.connect(generator,plant.getTermination('inputs')) # generator to plant
net.connect(model.getOrigin('output'), err, weight=-1) # model to error (negative value)
net.connect(plant.getOrigin('output'), err, weight=+1) # plant to error
net.connect(err,mmse.getTermination('error')) # actual error to MSE
return net;