A) feedback manner B) feedforward manner C) feedforward and feedback D) feedforward or feedback
A) input layer B) second layer C) hidden layer D) output 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 and Unsupervised B) UnSupervised C) Supervised
A) Artificial Resonance Theory B) Adaptive Resonance Theory C) Automatic Resonance Theory
A) Binary and Bipolar B) Binary 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 inverse mapping functions B) its ability to learn forward mapping functions C) its ability to learn forward and 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) Supervised B) UnSupervised C) Learning with critic
A) FALSE B) TRUE
A) inhibitory inpur B) excitatory input
A) deterministically B) both deterministically & stochastically C) stochastically
A) greater the degradation less is the activation value of other units B) greater the degradation less is the activation value of winning 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 & output signal B) learning laws which modulate difference between actual output & desired output C) learning laws which modulate difference between synaptic weight & activation value
A) the number of outputs B) the overall characteristics of the mapping problem C) the number of inputs
A) the number of inputs it can deliver B) the number of inputs it can take C) the number of patterns that can be stored
A) Slow process B) Fast process C) can be slow or fast in general |