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