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