This paper demonstrates a feedforward active substrate noise cancelling technique using a power supply di/dt detector. Since the substrate is usually tied with the ground line with a low impedance, the substrate noise is closely related to the ground bounce which is proportional to the di/dt when inductance is dominant on the ground line impedance. Our active cancelling detects the di/dt of the power supply, and injects an anti-phase current into the substrate so that the di/dt-proportional substrate noise is cancelled out. Our first trial shows that 34% substrate noise reduction is achieved on our test circuit, and the theoretical analysis shows that the optimized canceller design will enhance the substrate noise suppression ratio up to 56%.

This paper describes a method of extending the signal frequency bandwidth while increasing the stability of a CMOS transimpedance amplifier (TIA). The TIA consists of three inverting amplifiers in a series, and a high-pass filter plus a non-inverting amplifier that are connected to the last two inverting amplifiers stated above in parallel. The TIA is fabricated using a 0.35 µm CMOS process and realizes stable conversion of 60-dBΩ from the photodiode current to the output voltage with more than 500 MHz of signal frequency bandwidth and 60 mW of power consumption from a 3.3 V supply voltage.

Weight divided adaptive control method for a microwave FeedForward Power Amplifier (FFPA) is presented. In this adaptive controller, an output signal of a power amplifier is used as reference signal. Additionally, reference signal is divided by the weight of adaptive filter, so that characteristics of the power amplifier, such as temperature dependence, do not have influence on the convergence performances. The proposed adaptive algorithm and the convergence condition are derived analytically and we clarify that the proposed weight divided adaptive algorithm is more stable than the conventional Normalized Least Mean Square (NLMS) algorithm. Then, the convergence condition considering phase calibration error is discussed. The effectiveness of the proposed algorithm are also verified by the nonlinear simulations of the FFPA having AM-AM and AM-PM nonlinearity of GaAsFET.

Passification of a non-square linear system is considered by using a parallel feedforward compensator (PFC) and a squaring gain matrix. In contrast to the previous result, a technical assumption is removed by modifying the structure of the PFC. As a result, the broader class of non-square systems can be made passive by the proposed design method. Using the static output feedback (SOF) algorithms, the input-dimensional PFC and the squaring matrix can be designed systematically. The effectiveness of the proposed method is illustrated by practical system examples in the control literature.

This paper presents a frequency domain simultaneous equations method capable of automatically recovering noise reduction effect degraded by secondary path changes. The simultaneous equations method has been studied, first in time domain. Accordingly to the study, in the time domain, the simultaneous equations method requires an additional filter and a system identification circuit used for transforming the solution of the simultaneous equations into the coefficients of noise control filter, which increase the processing cost. To reduce the processing cost, this paper studies on the application of a frequency domain processing technique, the cross spectrum method, to the simultaneous equations method. By directly applying the equation defining the cross spectrum method to the solution, the additional filter becomes unnecessary. In addition, the system identification circuit is replaced with the inverse Fourier transform. Thereby, the processing cost drastically decreases. This paper also presents simulation results to confirm that the proposed method can automatically recover the noise reduction effect degraded by a path change and provides much higher convergence speed than that of the filtered-x NLMS algorithm with the perfectly modeled secondary path filter.

A high efficiency feedforward power amplifier (FFPA) with a series diode linearizer for cellular base stations is presented. In order to achieve the highest overall efficiency of an FFPA, an improved pre-distortion diode linearizer has been used and the bias condition of the main amplifier has been optimized. The optimum bias condition has been derived from the overall efficiency analysis of the FFPA with a pre-distortion linearizer. From measured overall performances of the FFPA, efficiency enhancement of the series diode linearizer has been verified. The developed FFPA achieved the efficiency of 10% and output power of 45.6 dBm at 10 MHz offset Adjacent Channel leakage Power Ratio (ACPR) -50 dBc under Wide-band Code-Division Multiple-Access (W-CDMA) modulated 2 carriers signal. This design method can be also used to optimize the source and load impedances condition of the main amplifier FET.

We present a state-space approach to the problem of designing a parallel feedforward compensator (PFC), which has the same dimension of the input i.e. input-dimensional, for a class of non-square linear systems such that the closed-loop system is strictly passive. For a non-minimum phase system or a system with high relative degree, passification of the system cannot be achieved by any other methodologies except by using a PFC. In our scheme, we first determine a squaring gain matrix and an additional dynamics that is connected to the system in a feedforward way, then a static passifying control law is designed. Consequently, the actual feedback controller will be the static control law combined with the feedforward dynamics. Necessary and sufficient conditions for the existence of the PFC are given by the static output feedback formulation, which enables to utilize linear matrix inequality (LMI). Since the proposed PFC is input-dimensional, our design procedure can be viewed as a solution to the low-order dynamic output feedback control problem in the literature. The effectiveness of the proposed method is illustrated by some numerical examples.

In this paper we propose a learning algorithm to enhance the fault tolerance of feedforward neural networks (NNs for short) by manipulating the gradient of sigmoid activation function of the neuron. We assume stuck-at-0 and stuck-at-1 faults of the connection link. For the output layer, we employ the function with the relatively gentle gradient to enhance its fault tolerance. For enhancing the fault tolerance of hidden layer, we steepen the gradient of function after convergence. The experimental results for a character recognition problem show that our NN is superior in fault tolerance, learning cycles and learning time to other NNs trained with the algorithms employing fault injection, forcible weight limit and the calculation of relevance of each weight to the output error. Besides the gradient manipulation incorporated in our algorithm never spoils the generalization ability.

To enhance fault tolerance ability of the feedforward neural networks (NNs for short) implemented in hardware, we discuss the learning algorithm that converges without adding extra neurons and a large amount of extra learning time and cycles. Our algorithm modified from the standard backpropagation algorithm (SBPA for short) limits synaptic weights of neurons in range during learning phase. The upper and lower bounds of the weights are calculated according to the average and standard deviation of them. Then our algorithm reupdates any weight beyond the calculated range to the upper or lower bound. Since the above enables us to decrease the standard deviation of the weights, it is useful in enhancing fault tolerance. We apply NNs trained with other algorithms and our one to a character recognition problem. It is shown that our one is superior to other ones in reliability, extra learning time and/or extra learning cycles. Besides we clarify that our algorithm never degrades the generalization ability of NNs although it coerces the weights within the calculated range.

In pattern recognition using neural networks, it is very difficult for researchers or users to design optimal neural network architecture for a specific task. It is possible for any kinds of neural network architectures to obtain a certain measure of recognition ratio. It is, however, difficult to get an optimal neural network architecture for a specific task analytically in the recognition ratio and effectiveness of training. In this paper, an evolutional method of training and designing feedforward neural networks is proposed. In the proposed method, a neural network is defined as one individual and neural networks whose architectures are same as one species. These networks are evaluated by normalized M. S. E. (Mean Square Error) which presents a performance of a network for training patterns. Then, their architectures evolve according to an evolution rule proposed here. Architectures of neural networks, in other words, species, are evaluated by another measurement of criteria compared with the criteria of individuals. The criteria assess the most superior individual in the species and the speed of evolution of the species. The species are increased or decreased in population size according to the criteria. The evolution rule generates a little bit different architectures of neural network from superior species. The proposed method, therefore, can generate variety of architectures of neural networks. The designing and training neural networks which performs simple 3 3 and 4 4 pixels which include vertical, horizontal and oblique lines classifications and Handwritten KATAKANA recognitions are presented. The efficiency of proposed method is also discussed.

In learning of feed-forward neural networks, so-called 'training error' is often minimized. This is, however, not related to the generalization capability which is one of the major goals in the learning. It can be interpreted as a substitute for another learning which considers the generalization capability. Admissibility is a concept to discuss whether a learning can be a substitute for another learning. In this paper, we discuss the case where the learning which minimizes a training error is used as a substitute for the projection learning, which considers the generalization capability, in the presence of noise. Moreover, we give a method for choosing a training set which satisfies the admissibility.

In this paper, a dynamic constructive algorithm for fault tolerant feedforward neural network, called DCFTA, is proposed. The algorithm starts with a network with single hidden neuron, and a new hidden unit is added dynamically to the network whenever it fails to converge. Before inserting the new hidden neuron into the network, only the weights connecting the new hidden neuron to the other neurons are trained (i. e. , updated) until there is no significant reduction of the output error. To generate a fault tolerant network, the relevance of each synaptic weight is estimated in each cycle, and only the weights which have their relevance less than a specified threshold are updated in that cycle. The loss of a connections between neurons (which are equivalent to stuck-at-0 faults) are assumed. The simulation results indicate that the network constructed by DCFTA has a significant fault tolerance ability.

Considering the pattern classification/recognition tasks, the influence of the activation function on fault tolerance property of feedforward neural networks is empirically investigated. The simulation results show that the activation function largely influences the fault tolerance and the generalization property of neural networks. It is found that, neural networks with symmetric sigmoid activation function are largely fault tolerant than the networks with asymmetric sigmoid function. However the close relation between the fault tolerance and the generalization property was not observed and the networks with asymmetric activation function slightly generalize better than the networks with the symmetric activation function. First, the influence of the activation function on fault tolerance property of neural networks is investigated on the XOR problem, then the results are generalized by evaluating the fault tolerance property of different NNs implementing different benchmark problems.

A new learning algorithm is proposed to enhance fault tolerance ability of the feedforward neural networks. The algorithm focuses on the links (weights) that may cause errors at the output when they are open faults. The relevances of the synaptic weights to the output error (i.e. the sensitivity of the output error to the weight fault) are estimated in each training cycle of the standard backpropagation using the Taylor expansion of the output around fault-free weights. Then the weight giving the maximum relevance is decreased. The approach taken by the algorithm described in this paper is to prevent the weights from having large relevances. The simulation results indicate that the network trained with the proposed algorithm do have significantly better fault tolerance than the network trained with the standard backpropagation algorithm. The simulation results show that the fault tolerance and the generalization abilities are improved.

Very-low-voltage 1.2-V mixed-signal CMOS technology is a device/circuit solution aimed at ultra-low-power portable systems such as digital cellular terminals and PDAs. We have developed an experimental 1.2-V mixed analog and digital LSI circuit/device technology. This technology is based on a new transistor structure that has a 0.3-µm gate length and a low Vth of 0.4 V, and that suppresses the short-channel effect. In this paper, we will mainly discuss low-voltage analog circuit design that uses this technology. We show that low Vth is essential not only to digital circuits, but also to 1.2-V analog amplifier, A/D converter and analog switch designs. To achieve high-conversion rate A/D converters, a pipeline architecture is used for low-voltage operation. To increase the attainable gain-bandwidth of the operational amplifier of the converter, a feedforward phase-compensated three-stage amplifier is proposed. The addition of a feedforward capacitor allows a high frequency signal to pass directly to the second stage, which optimizes use of the second stage bandwidth. Pole-zero canceling is used to achieve a fast settling of the amplifier. Although gain precision is degraded by the positive feedback through the feedforward capacitor, this can be offset by increasing the equivalent second-stage gain with an inner feedforward compensated amplifier. The gain-bandwidth of the proposed double feedforward amplifier is two to three times wider than with the conventional Miller compensation. With these techniques, we used 1.2-V mixed-signal CMOS technology to create a basic logic gate with a 400-ps delay and 0.4-µW/MHz power, and a 9-bit 2-Msample/s pipeline A/D converter with power dissipation of only 4 mW.

This paper proposes an approach for approximately realizing nonlinear mappings of interval vectors by interval neural networks. Interval neural networks in this paper are characterized by interval weights and interval biases. This means that the weights and biases are given by intervals instead of real numbers. First, an architecture of interval neural networks is proposed for dealing with interval input vectors. Interval neural networks with the proposed architecture map interval input vectors to interval output vectors by interval arithmetic. Some characteristic features of the nonlinear mappings realized by the interval neural networks are described. Next, a learning algorithm is derived. In the derived learning algorithm, training data are the pairs of interval input vectors and interval target vectors. Last, using a numerical example, the proposed approach is illustrated and compared with other approaches based on the standard back-propagation neural networks with real number weights.

Analysis of satellite images requires classificatio of image objects. Since different categories may have almost the same brightness or feature in high dimensional remote sensing data, many object categories overlap with each other. How to segment the object categories accurately is still an open question. It is widely recognized that the assumptions required by many classification methods (maximum likelihood estimation, etc.) are suspect for textural features based on image pixel brightness. We propose an image feature based neural network approach for the segmentation of AVHRR images. The learning algoriothm is a modified backpropagation with gain and weight decay, since feedforward networks using the backpropagation algorithm have been generally successful and enjoy wide popularity. Destructive algorithms that adapt the neural architecture during the training have been developed. The classification accuracy of 100% is reached for a validation data set. Classification result is compared with that of Kohonen's LVQ and basic backpropagation algorithm based pixel-by-pixel method. Visual investigation of the result images shows that our method can not only distinguish the categories with similar signatures very well, but also is robustic to noise.

An adaptive fuzzy network (AFN) is described that can be used to implement most of fuzzy logic functions. We introduce a learning algorithm largely borrowed from backpropagation algorithm and train the AFN system for several typical fuzzy problems. Simulations show that an adaptive fuzzy network can be implemented with the proposed network and algorithm, which would be impractical for a conventional fuzzy system.

We introduce a novel neural network called the T-Model and investigates the learning ability of the T-Model neural network. A learning algorithm based on the least mean square (LMS) algorithm is used to train the T-Model and produces a very good result for the T-Model network. We present simulation results on several practical problems to illustrate the efficiency of the learning techniques. As a result, the T-Model network learns successfully, but the Hopfield model fails to and the T-Model learns much more effectively and more quickly than a multi-layer network.