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