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