A) feedforward and feedback B) feedforward manner C) feedforward or feedback D) feedback manner
A) input layer B) output layer C) hidden layer D) second layer
A) may receive or give input or output to others B) gives output to all others C) receives inputs from all others
A) Supervised B) Supervised and Unsupervised C) UnSupervised
A) Artificial Resonance Theory B) Adaptive Resonance Theory C) Automatic Resonance Theory
A) Binary B) Bipolar C) Binary and Bipolar
A) Large Cluster B) No change C) Small cluster
A) feedforwward network with hidden layer B) two feedforward network with hidden layer C) feed forward network only
A) its ability to learn forward and inverse mapping functions B) its ability to learn forward mapping functions C) its ability to learn inverse mapping functions
A) each input unit is connected to each output unit B) some are connected C) all are one to one connected
A) UnSupervised B) Supervised C) Learning with critic
A) TRUE B) FALSE
A) inhibitory inpur B) excitatory input
A) both deterministically & stochastically B) stochastically C) deterministically
A) greater the degradation less is the activation value of winning units B) greater the degradation more is the activation value of winning units C) greater the degradation less is the activation value of other units
A) depends on type of clustering B) Yes C) No
A) learning laws which modulate difference between synaptic weight & activation value B) learning laws which modulate difference between actual output & desired output C) learning laws which modulate difference between synaptic weight & output signal
A) the number of outputs B) the overall characteristics of the mapping problem C) the number of inputs
A) the number of patterns that can be stored B) the number of inputs it can deliver C) the number of inputs it can take
A) can be slow or fast in general B) Fast process C) Slow process |