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